-----------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\finseraa\Dropbox\05 Group Appeals\replication archive\JOP Dataverse\jop_log.log
  log type:  text
 opened on:  11 Dec 2024, 13:34:56

. do "C:\Users\finseraa\Dropbox\05 Group Appeals\replication archive\JOP Dataverse\code.do"

. *THIS DO-FILE REPRODUCES THE RESULTS IN FINSERAAS, HEATH, LANGSÆTHER & SMETS (2024) "How Group Appeals Shape Group-Party Linkages
>  in Two Political Systems" (JOP).
. *To run the do-file without making any changes to the file you need to download the Stata data-files and save them on the same fo
> lder as this do-file.
. *Before running the file you need to install the packages coefplot and grc1leg2 using the command ssc install. To do this you nee
> d internet connection.
. *The code runs on Stata version 18
. 
. global dimensions_uk age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration policy_envi
> ronment policy_eu conflict_appeal solidarity_appeal

. 
. global dimensions_no age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration policy_envi
> ronment policy_abort conflict_appeal solidarity_appeal

. 
. global dimensions_interactions_uk age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigratio
> n policy_environment policy_eu  conflict_appeal solidarity_appeal age_candidateXwc gender_candidateXwc class_candidateXwc rural_c
> andidateXwc policy_welfareXwc policy_immigrationXwc policy_environmentXwc policy_euXwc conflict_appealXwc solidarity_appealXwc

. 
. global dimensions_interactions_no age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigratio
> n policy_environment policy_abort  conflict_appeal solidarity_appeal age_candidateXwc gender_candidateXwc class_candidateXwc rura
> l_candidateXwc policy_welfareXwc policy_immigrationXwc policy_environmentXwc policy_abortXwc conflict_appealXwc solidarity_appeal
> Xwc

. 
. ********************************************************************************
. *FIGURE 1***********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg selected $dimensions_no if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,556
                                                F(10, 647)        =       2.42
                                                Prob > F          =     0.0078
                                                R-squared         =     0.0088
                                                Root MSE          =     .49888

                           (Std. err. adjusted for 648 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0112875   .0203252    -0.56   0.579    -.0511987    .0286238
  gender_candidate |  -.0521848   .0194698    -2.68   0.008    -.0904165   -.0139531
   class_candidate |  -.0143095   .0196914    -0.73   0.468    -.0529762    .0243572
   rural_candidate |   .0223278   .0198176     1.13   0.260    -.0165868    .0612423
    policy_welfare |   .0288015   .0208467     1.38   0.168    -.0121337    .0697368
policy_immigration |   .0323381   .0198351     1.63   0.104    -.0066108     .071287
policy_environment |  -.0297958   .0204992    -1.45   0.147    -.0700488    .0104571
      policy_abort |  -.0103475   .0200152    -0.52   0.605      -.04965    .0289551
   conflict_appeal |  -.0680334   .0277445    -2.45   0.014    -.1225136   -.0135532
 solidarity_appeal |  -.0448457   .0284021    -1.58   0.115    -.1006171    .0109257
             _cons |   .5621372   .0340969    16.49   0.000     .4951832    .6290912
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_no if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,564
                                                F(10, 653)        =       3.29
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0122
                                                Root MSE          =       .498

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0353682    .020003    -1.77   0.078    -.0746461    .0039097
  gender_candidate |   .0015086   .0196258     0.08   0.939    -.0370286    .0400458
   class_candidate |    .022302   .0202544     1.10   0.271    -.0174695    .0620736
   rural_candidate |   .0081838   .0206073     0.40   0.691    -.0322807    .0486482
    policy_welfare |   .0583353   .0206247     2.83   0.005     .0178366    .0988341
policy_immigration |     .04874    .020156     2.42   0.016     .0091615    .0883185
policy_environment |   .0516881   .0200286     2.58   0.010       .01236    .0910162
      policy_abort |  -.0075317   .0201419    -0.37   0.709    -.0470824     .032019
   conflict_appeal |   .0436032   .0282084     1.55   0.123     -.011787    .0989934
 solidarity_appeal |   .0042447   .0272303     0.16   0.876    -.0492249    .0577142
             _cons |   .4046002   .0352391    11.48   0.000     .3354046    .4737958
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid) xsc(r(-.2 .2)) xlab(
> -.2 (.1) .2) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) titl
> e("AMCE by class", size(medsmall))

. graph save "amce_class_no.gph", replace
file amce_class_no.gph saved

. 
. reg selected $dimensions_interactions_no workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      5,120
                                                F(21, 1301)       =       2.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0105
                                                Root MSE          =     .49844

                            (Std. err. adjusted for 1,302 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |  -.0112875   .0203192    -0.56   0.579    -.0511495    .0285745
     gender_candidate |  -.0521848   .0194641    -2.68   0.007    -.0903693   -.0140003
      class_candidate |  -.0143095   .0196856    -0.73   0.467    -.0529285    .0243095
      rural_candidate |   .0223278   .0198118     1.13   0.260    -.0165387    .0611943
       policy_welfare |   .0288015   .0208405     1.38   0.167    -.0120832    .0696863
   policy_immigration |   .0323381   .0198293     1.63   0.103    -.0065627     .071239
   policy_environment |  -.0297958   .0204932    -1.45   0.146    -.0699991    .0104074
         policy_abort |  -.0103475   .0200093    -0.52   0.605    -.0496015    .0289066
      conflict_appeal |  -.0680334   .0277364    -2.45   0.014    -.1224463   -.0136205
    solidarity_appeal |  -.0448457   .0283938    -1.58   0.114    -.1005483    .0108569
     age_candidateXwc |  -.0240808    .028509    -0.84   0.398    -.0800095    .0318479
  gender_candidateXwc |   .0536934   .0276371     1.94   0.052    -.0005247    .1079115
   class_candidateXwc |   .0366116   .0282406     1.30   0.195    -.0187906    .0920137
   rural_candidateXwc |   -.014144   .0285819    -0.49   0.621    -.0702157    .0419277
    policy_welfareXwc |   .0295338   .0293167     1.01   0.314    -.0279794     .087047
policy_immigrationXwc |   .0164019   .0282708     0.58   0.562    -.0390595    .0718632
policy_environmentXwc |   .0814839   .0286511     2.84   0.005     .0252764    .1376914
      policy_abortXwc |   .0028158   .0283874     0.10   0.921    -.0528742    .0585058
   conflict_appealXwc |   .1116366   .0395547     2.82   0.005     .0340385    .1892347
 solidarity_appealXwc |   .0490903   .0393355     1.25   0.212    -.0280776    .1262583
         workingclass |   -.157537   .0490206    -3.21   0.001     -.253705    -.061369
                _cons |   .5621372   .0340869    16.49   0.000     .4952658    .6290086
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_no workingclass) ylabel(, nogrid) xsc(r(-.1 .2)) xlab(-.1 (.1) .2) xline(0, lpat(shortd
> ash_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by class", size(medsmall)
> ) 

. graph save "amce_diff_no.gph", replace
file amce_diff_no.gph saved

. 
. clear 

. use  "Data_Britain.dta"

. 
. reg selected $dimensions_uk if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      3,848
                                                F(10, 961)        =       8.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0234
                                                Root MSE          =     .49483

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0098526   .0164508    -0.60   0.549    -.0421363    .0224311
  gender_candidate |  -.0078076   .0161554    -0.48   0.629    -.0395115    .0238964
   class_candidate |  -.0224832   .0162746    -1.38   0.167    -.0544212    .0094547
   rural_candidate |   -.000638   .0164552    -0.04   0.969    -.0329304    .0316544
    policy_welfare |   .0429343   .0168801     2.54   0.011     .0098082    .0760603
policy_immigration |   .0493925   .0164943     2.99   0.003     .0170234    .0817616
policy_environment |  -.1094397   .0169867    -6.44   0.000     -.142775   -.0761044
         policy_eu |     -.0604   .0167837    -3.60   0.000     -.093337    -.027463
   conflict_appeal |  -.0644962   .0226456    -2.85   0.004    -.1089367   -.0200557
 solidarity_appeal |  -.0722826   .0226208    -3.20   0.001    -.1166744   -.0278908
             _cons |   .6127742    .028495    21.50   0.000     .5568545    .6686938
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_uk if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      4,156
                                                F(10, 1038)       =      11.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0270
                                                Root MSE          =     .49386

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0168113   .0157426    -1.07   0.286    -.0477023    .0140797
  gender_candidate |  -.0489108   .0149497    -3.27   0.001    -.0782458   -.0195758
   class_candidate |   .0551977   .0155672     3.55   0.000     .0246509    .0857444
   rural_candidate |  -.0060767   .0153337    -0.40   0.692    -.0361654    .0240119
    policy_welfare |   .0505888   .0157071     3.22   0.001     .0197675    .0814101
policy_immigration |   .1104905   .0161335     6.85   0.000     .0788325    .1421485
policy_environment |  -.0628502   .0156778    -4.01   0.000    -.0936139   -.0320864
         policy_eu |     -.0187   .0161316    -1.16   0.247    -.0503542    .0129542
   conflict_appeal |   .0412409    .021548     1.91   0.056    -.0010417    .0835235
 solidarity_appeal |  -.0052955   .0219942    -0.24   0.810    -.0484538    .0378627
             _cons |   .4555079   .0271251    16.79   0.000     .4022817    .5087342
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid) xsc(r(-.2 .2)) xlab(
> -.2 (.1) .2) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) titl
> e("AMCE by class", size(medsmall))

. graph save "amce_class_uk.gph", replace
file amce_class_uk.gph saved

.  
. reg selected $dimensions_interactions_uk workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(21, 2000)       =       9.77
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0253
                                                Root MSE          =     .49432

                            (Std. err. adjusted for 2,001 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |  -.0098526   .0164466    -0.60   0.549    -.0421068    .0224016
     gender_candidate |  -.0078076   .0161512    -0.48   0.629    -.0394825    .0238674
      class_candidate |  -.0224832   .0162705    -1.38   0.167     -.054392    .0094256
      rural_candidate |   -.000638    .016451    -0.04   0.969    -.0329009    .0316249
       policy_welfare |   .0429343   .0168757     2.54   0.011     .0098384    .0760301
   policy_immigration |   .0493925   .0164901     3.00   0.003     .0170529    .0817321
   policy_environment |  -.1094397   .0169823    -6.44   0.000    -.1427446   -.0761348
            policy_eu |     -.0604   .0167794    -3.60   0.000    -.0933069    -.027493
      conflict_appeal |  -.0644962   .0226397    -2.85   0.004    -.1088961   -.0200963
    solidarity_appeal |  -.0722826   .0226149    -3.20   0.001    -.1166339   -.0279313
     age_candidateXwc |  -.0069587   .0227653    -0.31   0.760    -.0516049    .0376875
  gender_candidateXwc |  -.0411033   .0220068    -1.87   0.062    -.0842619    .0020554
   class_candidateXwc |   .0776809   .0225168     3.45   0.001     .0335221    .1218397
   rural_candidateXwc |  -.0054387   .0224878    -0.24   0.809    -.0495407    .0386632
    policy_welfareXwc |   .0076546    .023053     0.33   0.740    -.0375559    .0528651
policy_immigrationXwc |    .061098   .0230684     2.65   0.008     .0158574    .1063385
policy_environmentXwc |   .0465895   .0231113     2.02   0.044     .0012648    .0919142
         policy_euXwc |      .0417   .0232747     1.79   0.073    -.0039453    .0873452
   conflict_appealXwc |   .1057371   .0312532     3.38   0.001      .044445    .1670293
 solidarity_appealXwc |    .066987   .0315446     2.12   0.034     .0051233    .1288508
         workingclass |  -.1572662   .0393337    -4.00   0.000    -.2344055    -.080127
                _cons |   .6127742   .0284877    21.51   0.000     .5569055    .6686428
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_uk workingclass) ylabel(, nogrid) xsc(r(-.1 .2)) xlab(-.1 (.1) .2)   xline(0, lpat(shor
> tdash_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by class", size(medsmal
> l)) 

. graph save "amce_diff_uk.gph", replace
file amce_diff_uk.gph saved

. 
. 
. gr combine "amce_class_no.gph" "amce_diff_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "main_no.gph", replace
file main_no.gph saved

. gr combine "amce_class_uk.gph" "amce_diff_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "main_uk.gph", replace
file main_uk.gph saved

. grc1leg2 "main_no.gph" "main_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figure1.gph", replace
file figure1.gph saved

. gr export  "figure1.pdf",as(pdf) replace
file figure1.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *FIGURE 2***********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg rightrating $dimensions_no if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,589
                                                F(10, 653)        =      68.00
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2226
                                                Root MSE          =     1.1438

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0824923   .0446621     1.85   0.065    -.0052064     .170191
  gender_candidate |   .0749511   .0447076     1.68   0.094    -.0128369    .1627391
   class_candidate |  -.1047124   .0452397    -2.31   0.021    -.1935453   -.0158795
   rural_candidate |  -.0804114   .0462899    -1.74   0.083    -.1713063    .0104835
    policy_welfare |  -.8783772    .050988   -17.23   0.000    -.9784975   -.7782569
policy_immigration |   .6843187   .0511647    13.37   0.000     .5838516    .7847859
policy_environment |   .3381674   .0459211     7.36   0.000     .2479966    .4283382
      policy_abort |  -.2716288   .0448365    -6.06   0.000    -.3596699   -.1835877
   conflict_appeal |  -.3273727   .0595474    -5.50   0.000    -.4443003   -.2104452
 solidarity_appeal |   -.058465    .059836    -0.98   0.329    -.1759591    .0590291
             _cons |   4.165507    .080776    51.57   0.000     4.006895    4.324119
------------------------------------------------------------------------------------

. est store wc0

. reg rightrating $dimensions_no if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,578
                                                F(10, 652)        =      24.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1108
                                                Root MSE          =     1.2298

                           (Std. err. adjusted for 653 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0704106   .0495913     1.42   0.156    -.0269673    .1677884
  gender_candidate |    .004796   .0479975     0.10   0.920    -.0894524    .0990444
   class_candidate |  -.1208142   .0506696    -2.38   0.017    -.2203095   -.0213188
   rural_candidate |  -.1385796   .0480408    -2.88   0.004    -.2329129   -.0442463
    policy_welfare |  -.4613761   .0557083    -8.28   0.000    -.5707655   -.3519868
policy_immigration |   .6463777   .0530199    12.19   0.000     .5422673     .750488
policy_environment |   .1618892   .0499751     3.24   0.001     .0637577    .2600208
      policy_abort |  -.0611133   .0494904    -1.23   0.217    -.1582931    .0360664
   conflict_appeal |  -.2630388   .0685731    -3.84   0.000    -.3976895   -.1283881
 solidarity_appeal |  -.0141249   .0671165    -0.21   0.833    -.1459155    .1176657
             _cons |   4.101699   .0823688    49.80   0.000     3.939959    4.263439
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid)  xline(0, lpat(short
> dash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Rightscale: AMCE by class", s
> ize(medsmall))

. graph save "amce_rightrating_class_no.gph", replace
file amce_rightrating_class_no.gph saved

.  
. reg represented $dimensions_no if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,589
                                                F(10, 651)        =       3.74
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0171
                                                Root MSE          =     1.3529

                           (Std. err. adjusted for 652 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0105508   .0562229     0.19   0.851    -.0998492    .1209508
  gender_candidate |  -.0065184   .0536044    -0.12   0.903    -.1117767      .09874
   class_candidate |  -.1378516   .0529221    -2.60   0.009    -.2417702   -.0339331
   rural_candidate |  -.0677817   .0554365    -1.22   0.222    -.1766376    .0410743
    policy_welfare |   .2792299   .0617016     4.53   0.000     .1580718     .400388
policy_immigration |  -.0073837   .0606846    -0.12   0.903    -.1265449    .1117775
policy_environment |   .0637773   .0565373     1.13   0.260    -.0472403    .1747948
      policy_abort |  -.0665643   .0574895    -1.16   0.247    -.1794514    .0463229
   conflict_appeal |  -.1566237   .0767672    -2.04   0.042    -.3073649   -.0058826
 solidarity_appeal |    -.05972   .0740994    -0.81   0.421    -.2052226    .0857827
             _cons |   3.587907   .1057286    33.94   0.000     3.380297    3.795517
------------------------------------------------------------------------------------

. est store wc0

. reg represented $dimensions_no if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,600
                                                F(10, 655)        =       3.35
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0131
                                                Root MSE          =     1.4661

                           (Std. err. adjusted for 656 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0904874   .0580817    -1.56   0.120    -.2045363    .0235614
  gender_candidate |  -.0673825   .0573186    -1.18   0.240     -.179933    .0451679
   class_candidate |   -.002678   .0576568    -0.05   0.963    -.1158924    .1105364
   rural_candidate |   -.046429   .0567322    -0.82   0.413    -.1578279    .0649699
    policy_welfare |   .1687537   .0644535     2.62   0.009     .0421933    .2953141
policy_immigration |   .1813021    .066904     2.71   0.007     .0499298    .3126743
policy_environment |   .1474283   .0593899     2.48   0.013     .0308107    .2640458
      policy_abort |  -.0350425   .0631546    -0.55   0.579    -.1590523    .0889673
   conflict_appeal |   .0323752    .081816     0.40   0.692     -.128278    .1930284
 solidarity_appeal |  -.0977146   .0780837    -1.25   0.211    -.2510393      .05561
             _cons |   3.511168   .1059618    33.14   0.000     3.303102    3.719233
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid)  xline(0, lpat(short
> dash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Represented: AMCE by class", 
> size(medsmall))

. graph save "amce_represented_class_no.gph", replace
file amce_represented_class_no.gph saved

.  
. 
. clear 

. use  "Data_Britain.dta"

.  
. reg rightrating $dimensions_uk if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      3,848
                                                F(10, 961)        =      28.95
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0963
                                                Root MSE          =     1.3204

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0350976   .0435662     0.81   0.421    -.0503982    .1205934
  gender_candidate |   .0425821   .0412851     1.03   0.303    -.0384373    .1236015
   class_candidate |  -.1932184   .0441346    -4.38   0.000    -.2798296   -.1066071
   rural_candidate |     .03958   .0431662     0.92   0.359    -.0451309    .1242909
    policy_welfare |  -.4506763   .0461823    -9.76   0.000    -.5413061   -.3600465
policy_immigration |   .5300875   .0473091    11.20   0.000     .4372465    .6229284
policy_environment |   .3280157   .0442827     7.41   0.000     .2411137    .4149176
         policy_eu |   .2335652   .0431533     5.41   0.000     .1488796    .3182507
   conflict_appeal |   -.315522    .062467    -5.05   0.000    -.4381093   -.1929346
 solidarity_appeal |  -.1083193   .0580485    -1.87   0.062    -.2222357    .0055971
             _cons |   3.954819   .0836044    47.30   0.000      3.79075    4.118887
------------------------------------------------------------------------------------

. est store wc0

. reg rightrating $dimensions_uk if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      4,156
                                                F(10, 1038)       =      20.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0588
                                                Root MSE          =     1.3763

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0521653   .0404621    -1.29   0.198    -.1315622    .0272316
  gender_candidate |    .073297    .042788     1.71   0.087    -.0106638    .1572577
   class_candidate |   -.196902   .0445449    -4.42   0.000    -.2843103   -.1094937
   rural_candidate |   .0852645   .0423706     2.01   0.044     .0021227    .1684062
    policy_welfare |  -.2940168   .0470745    -6.25   0.000    -.3863888   -.2016447
policy_immigration |    .483743   .0463967    10.43   0.000     .3927011     .574785
policy_environment |   .1447446   .0450977     3.21   0.001     .0562515    .2332378
         policy_eu |   .1863202    .044194     4.22   0.000     .0996004    .2730401
   conflict_appeal |  -.2650244   .0601146    -4.41   0.000    -.3829844   -.1470643
 solidarity_appeal |  -.1525077   .0607174    -2.51   0.012    -.2716506   -.0333648
             _cons |   4.009406   .0747503    53.64   0.000     3.862727    4.156085
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid)  xsc(r(-1 1)) xlab(-
> 1 (.5) 1) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("
> Rightscale: AMCE by class", size(medsmall))

. graph save "amce_rightrating_class_uk.gph", replace
file amce_rightrating_class_uk.gph saved

.  
.  
. reg represented $dimensions_uk if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      3,848
                                                F(10, 961)        =       4.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0150
                                                Root MSE          =      1.498

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0066087   .0491664     0.13   0.893    -.0898773    .1030947
  gender_candidate |   .0197974   .0499995     0.40   0.692    -.0783233    .1179182
   class_candidate |  -.1142874   .0480889    -2.38   0.018    -.2086587   -.0199161
   rural_candidate |   .0293545   .0481811     0.61   0.543    -.0651978    .1239067
    policy_welfare |   .1735569   .0535701     3.24   0.001      .068429    .2786848
policy_immigration |   .0338651    .055007     0.62   0.538    -.0740826    .1418128
policy_environment |  -.2599986   .0508325    -5.11   0.000    -.3597542   -.1602431
         policy_eu |  -.1378337   .0504796    -2.73   0.006    -.2368967   -.0387706
   conflict_appeal |  -.0517714   .0697243    -0.74   0.458     -.188601    .0850581
 solidarity_appeal |  -.0731968   .0680051    -1.08   0.282    -.2066525    .0602589
             _cons |   3.782728   .0935019    40.46   0.000     3.599237     3.96622
------------------------------------------------------------------------------------

. est store wc0

. reg represented $dimensions_uk if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      4,156
                                                F(10, 1038)       =       7.73
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0188
                                                Root MSE          =     1.5285

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0120551   .0482741    -0.25   0.803     -.106781    .0826709
  gender_candidate |  -.0832864   .0475784    -1.75   0.080    -.1766471    .0100744
   class_candidate |   .1996822   .0485185     4.12   0.000     .1044767    .2948876
   rural_candidate |   .0490127   .0483134     1.01   0.311    -.0457905    .1438158
    policy_welfare |    .114414   .0522911     2.19   0.029     .0118058    .2170223
policy_immigration |   .2541115   .0529723     4.80   0.000     .1501664    .3580566
policy_environment |  -.2203205   .0495154    -4.45   0.000    -.3174822   -.1231588
         policy_eu |  -.0063887   .0518047    -0.12   0.902    -.1080426    .0952652
   conflict_appeal |   .0661222   .0684376     0.97   0.334    -.0681696     .200414
 solidarity_appeal |   .0362303   .0705532     0.51   0.608     -.102213    .1746735
             _cons |   3.584774   .0917599    39.07   0.000     3.404718     3.76483
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid)  xline(0, lpat(short
> dash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Represented: AMCE by class", 
> size(medsmall))

. graph save "amce_represented_class_uk.gph", replace
file amce_represented_class_uk.gph saved

. 
. 
. gr combine "amce_rightrating_class_no.gph"  "amce_represented_class_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "rightscale_represented_no.gph", replace
file rightscale_represented_no.gph saved

. gr combine "amce_rightrating_class_uk.gph"  "amce_represented_class_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "rightscale_represented_uk.gph", replace
file rightscale_represented_uk.gph saved

. grc1leg2 "rightscale_represented_no.gph" "rightscale_represented_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

.  
. gr save "figure2.gph", replace
file figure2.gph saved

. gr export  "figure2.pdf",as(pdf) replace
file figure2.pdf saved as PDF format

. 
. 
. 
. ********************************************************************************
. *FIGURE A2**********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. foreach x in $dimensions_no class_appeal2{
  2.         reg selected i.`x'  if workingclass==0,  cl(RespondentSerial)
  3.         margins i.`x', post
  4.         est store `x'0
  5.         reg selected i.`x'  if workingclass==1,  cl(RespondentSerial)
  6.         margins i.`x', post
  7.         est store `x'1
  8. } 

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       0.43
                                                Prob > F          =     0.5134
                                                R-squared         =     0.0002
                                                Root MSE          =     .50015

                      (Std. err. adjusted for 648 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
     selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |   -.013312   .0203586    -0.65   0.513    -.0532889    .0266649
        _cons |   .5068383   .0104596    48.46   0.000     .4862994    .5273772
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   .5068383   .0104596    48.46   0.000     .4862994    .5273772
          60  |   .4935263   .0099007    49.85   0.000     .4740849    .5129676
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       3.21
                                                Prob > F          =     0.0738
                                                R-squared         =     0.0013
                                                Root MSE          =     .49987

                      (Std. err. adjusted for 654 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
     selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |  -.0358885   .0200438    -1.79   0.074    -.0752466    .0034696
        _cons |   .5176923   .0098982    52.30   0.000     .4982562    .5371284
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   .5176923   .0098982    52.30   0.000     .4982562    .5371284
          60  |   .4818038    .010157    47.44   0.000     .4618594    .5017482
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       7.04
                                                Prob > F          =     0.0082
                                                R-squared         =     0.0027
                                                Root MSE          =     .49953

                         (Std. err. adjusted for 648 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |  -.0516452   .0194613    -2.65   0.008    -.0898602   -.0134303
           _cons |    .525661   .0096785    54.31   0.000     .5066559     .544666
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |    .525661   .0096785    54.31   0.000     .5066559     .544666
           Male  |   .4740157   .0098096    48.32   0.000     .4547533    .4932782
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       0.01
                                                Prob > F          =     0.9370
                                                R-squared         =     0.0000
                                                Root MSE          =     .50019

                         (Std. err. adjusted for 654 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |   .0015602   .0197229     0.08   0.937    -.0371679    .0402882
           _cons |    .499226   .0097836    51.03   0.000     .4800149    .5184372
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |    .499226   .0097836    51.03   0.000     .4800149    .5184372
           Male  |   .5007862   .0099393    50.38   0.000     .4812692    .5203031
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       0.51
                                                Prob > F          =     0.4773
                                                R-squared         =     0.0002
                                                Root MSE          =     .50015

                        (Std. err. adjusted for 648 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
       selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |  -.0140873    .019811    -0.71   0.477    -.0529889    .0248143
          _cons |   .5071429    .010042    50.50   0.000     .4874239    .5268618
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   .5071429    .010042    50.50   0.000     .4874239    .5268618
 Working class  |   .4930556    .009771    50.46   0.000     .4738689    .5122422
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       1.42
                                                Prob > F          =     0.2331
                                                R-squared         =     0.0006
                                                Root MSE          =     .50005

                        (Std. err. adjusted for 654 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
       selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |   .0242193   .0202927     1.19   0.233    -.0156274     .064066
          _cons |   .4874086   .0105542    46.18   0.000     .4666843    .5081329
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   .4874086   .0105542    46.18   0.000     .4666843    .5081329
 Working class  |   .5116279   .0097439    52.51   0.000     .4924947    .5307611
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       1.14
                                                Prob > F          =     0.2866
                                                R-squared         =     0.0004
                                                Root MSE          =     .50008

                        (Std. err. adjusted for 648 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
       selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |   .0211605    .019842     1.07   0.287     -.017802    .0601229
          _cons |   .4889976   .0103172    47.40   0.000     .4687383    .5092568
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   .4889976   .0103172    47.40   0.000     .4687383    .5092568
         Rural  |    .510158   .0095296    53.53   0.000     .4914454    .5288706
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       0.27
                                                Prob > F          =     0.6003
                                                R-squared         =     0.0001
                                                Root MSE          =     .50017

                        (Std. err. adjusted for 654 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
       selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |   .0109371   .0208613     0.52   0.600    -.0300262    .0519003
          _cons |   .4943182   .0108353    45.62   0.000      .473042    .5155944
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   .4943182   .0108353    45.62   0.000      .473042    .5155944
         Rural  |   .5052553    .010027    50.39   0.000     .4855662    .5249443
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       1.84
                                                Prob > F          =     0.1751
                                                R-squared         =     0.0008
                                                Root MSE          =         .5

                              (Std. err. adjusted for 648 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |   .0281701   .0207503     1.36   0.175    -.0125759    .0689161
                _cons |   .4860031   .0103196    47.10   0.000     .4657392     .506267
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   .4860031   .0103196    47.10   0.000     .4657392     .506267
Expand welfare state  |   .5141732   .0104376    49.26   0.000     .4936776    .5346688
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       8.77
                                                Prob > F          =     0.0032
                                                R-squared         =     0.0037
                                                Root MSE          =     .49927

                              (Std. err. adjusted for 654 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |   .0608438   .0205492     2.96   0.003     .0204933    .1011942
                _cons |   .4694357   .0103356    45.42   0.000     .4491406    .4897309
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   .4694357   .0103356    45.42   0.000     .4491406    .4897309
Expand welfare state  |   .5302795   .0102509    51.73   0.000     .5101509    .5504081
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       2.89
                                                Prob > F          =     0.0896
                                                R-squared         =     0.0011
                                                Root MSE          =     .49991

                              (Std. err. adjusted for 648 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .0336463   .0197899     1.70   0.090    -.0052139    .0725066
                _cons |     .48319   .0098848    48.88   0.000     .4637799    .5026001
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |     .48319   .0098848    48.88   0.000     .4637799    .5026001
Restrict immigration  |   .5168363   .0099169    52.12   0.000     .4973631    .5363096
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       6.17
                                                Prob > F          =     0.0132
                                                R-squared         =     0.0025
                                                Root MSE          =     .49957

                              (Std. err. adjusted for 654 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .0499458   .0201002     2.48   0.013      .010477    .0894147
                _cons |   .4744817   .0103106    46.02   0.000     .4542357    .4947276
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   .4744817   .0103106    46.02   0.000     .4542357    .4947276
Restrict immigration  |   .5244275   .0098142    53.44   0.000     .5051563    .5436987
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       1.89
                                                Prob > F          =     0.1696
                                                R-squared         =     0.0008
                                                Root MSE          =         .5

                           (Std. err. adjusted for 648 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Decrease CO2 tax  |  -.0281715   .0204874    -1.38   0.170    -.0684012    .0120582
             _cons |   .5139535   .0101415    50.68   0.000     .4940393    .5338677
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Increase CO2 tax  |   .5139535   .0101415    50.68   0.000     .4940393    .5338677
 Decrease CO2 tax  |    .485782   .0103537    46.92   0.000     .4654511    .5061129
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       7.00
                                                Prob > F          =     0.0083
                                                R-squared         =     0.0028
                                                Root MSE          =     .49949

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Decrease CO2 tax  |   .0531229   .0200778     2.65   0.008     .0136981    .0925478
             _cons |   .4724026   .0104349    45.27   0.000     .4519126    .4928926
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Increase CO2 tax  |   .4724026   .0104349    45.27   0.000     .4519126    .4928926
 Decrease CO2 tax  |   .5255255   .0096697    54.35   0.000     .5065381    .5445129
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       0.34
                                                Prob > F          =     0.5595
                                                R-squared         =     0.0001
                                                Root MSE          =     .50016

                               (Std. err. adjusted for 648 clusters in RespondentSerial)
----------------------------------------------------------------------------------------
                       |               Robust
              selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
More liberal abortion  |  -.0117558   .0201346    -0.58   0.560    -.0512928    .0277812
                 _cons |   .5056433   .0096627    52.33   0.000     .4866693    .5246174
----------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
No change in abortion  |   .5056433   .0096627    52.33   0.000     .4866693    .5246174
More liberal abortion  |   .4938875   .0104731    47.16   0.000     .4733221     .514453
----------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       0.12
                                                Prob > F          =     0.7286
                                                R-squared         =     0.0000
                                                Root MSE          =     .50018

                               (Std. err. adjusted for 654 clusters in RespondentSerial)
----------------------------------------------------------------------------------------
                       |               Robust
              selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
More liberal abortion  |  -.0070204   .0202243    -0.35   0.729    -.0467329    .0326921
                 _cons |   .5034965   .0100726    49.99   0.000     .4837179    .5232752
----------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
No change in abortion  |   .5034965   .0100726    49.99   0.000     .4837179    .5232752
More liberal abortion  |   .4964761   .0101521    48.90   0.000     .4765414    .5164109
----------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       3.62
                                                Prob > F          =     0.0575
                                                R-squared         =     0.0013
                                                Root MSE          =     .49986

                         (Std. err. adjusted for 648 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |  -.0373787   .0196436    -1.90   0.058    -.0759515    .0011941
           _cons |   .5150919   .0079245    65.00   0.000     .4995311    .5306526
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   .5150919   .0079245    65.00   0.000     .4995311    .5306526
   Conflict appeal  |   .4777132   .0117338    40.71   0.000     .4546723    .5007541
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       3.79
                                                Prob > F          =     0.0520
                                                R-squared         =     0.0015
                                                Root MSE          =     .49981

                         (Std. err. adjusted for 654 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |   .0399818   .0205368     1.95   0.052    -.0003442    .0803079
           _cons |   .4842037   .0081429    59.46   0.000     .4682142    .5001933
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   .4842037   .0081429    59.46   0.000     .4682142    .5001933
   Conflict appeal  |   .5241856   .0124086    42.24   0.000     .4998199    .5485512
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(1, 647)         =       0.01
                                                Prob > F          =     0.9361
                                                R-squared         =     0.0000
                                                Root MSE          =      .5002

                           (Std. err. adjusted for 648 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |   .0016272   .0202775     0.08   0.936    -.0381904    .0414448
             _cons |   .4993455   .0081563    61.22   0.000     .4833295    .5153616
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   .4993455   .0081563    61.22   0.000     .4833295    .5153616
   Solidarity appeal  |   .5009728   .0121212    41.33   0.000     .4771712    .5247743
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(1, 653)         =       1.39
                                                Prob > F          =     0.2389
                                                R-squared         =     0.0005
                                                Root MSE          =     .50006

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |  -.0235226   .0199538    -1.18   0.239    -.0627039    .0156588
             _cons |   .5094586   .0080198    63.52   0.000     .4937108    .5252064
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   .5094586   .0080198    63.52   0.000     .4937108    .5252064
   Solidarity appeal  |    .485936   .0119393    40.70   0.000      .462492      .50938
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,556
                                                F(2, 647)         =       2.93
                                                Prob > F          =     0.0539
                                                R-squared         =     0.0023
                                                Root MSE          =     .49971

                           (Std. err. adjusted for 648 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |  -.0666417   .0276425    -2.41   0.016    -.1209215   -.0123618
Solidarity appeal  |  -.0433821   .0284011    -1.53   0.127    -.0991515    .0123873
                   |
             _cons |   .5443548    .020833    26.13   0.000     .5034464    .5852633
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,556
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   .5443548    .020833    26.13   0.000     .5034464    .5852633
  Conflict appeal  |   .4777132   .0117361    40.70   0.000     .4546678    .5007586
Solidarity appeal  |   .5009728   .0121235    41.32   0.000     .4771665     .524779
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,564
                                                F(2, 653)         =       1.90
                                                Prob > F          =     0.1506
                                                R-squared         =     0.0015
                                                Root MSE          =     .49991

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |   .0434164   .0280175     1.55   0.122    -.0115989    .0984316
Solidarity appeal  |   .0051668   .0271689     0.19   0.849    -.0481822    .0585157
                   |
             _cons |   .4807692   .0201466    23.86   0.000     .4412092    .5203292
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,564
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   .4807692   .0201466    23.86   0.000     .4412092    .5203292
  Conflict appeal  |   .5241856   .0124111    42.24   0.000     .4998152     .548556
Solidarity appeal  |    .485936   .0119416    40.69   0.000     .4624874    .5093846
------------------------------------------------------------------------------------

.  
. 
. coefplot  (age_candidate0    gender_candidate0 class_candidate0  rural_candidate0  policy_welfare0  policy_immigration0  policy_e
> nvironment0  policy_abort0  class_appeal20, label(Middle class) color(black))  (age_candidate1    gender_candidate1 class_candida
> te1  rural_candidate1  policy_welfare1  policy_immigration1  policy_environment1  policy_abort1 class_appeal21, label(Working cla
> ss)), drop(_cons)  legend(off) xsc(r(.4 .6)) xlab(.4 (.05) .6) xline(.5, lpat(shortdash_dot)) plotregion(style(none)) lwidth(vvth
> in) ms(O) scale(.8) title("Marginal means by class", size(medsmall))  

. graph save "mm_class_no.gph", replace
file mm_class_no.gph saved

. 
. 
. foreach x in $dimensions_no turnout_appeal{
  2.         reg selected workingclass if `x'==0, cl(RespondentSerial)
  3.         est store `x'0
  4.         reg selected workingclass if `x'==1, cl(RespondentSerial)
  5.         est store `x'1
  6. }

Linear regression                               Number of obs     =      2,543
                                                F(1, 1213)        =       0.57
                                                Prob > F          =     0.4510
                                                R-squared         =     0.0001
                                                Root MSE          =     .50001

                   (Std. err. adjusted for 1,214 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |    .010854   .0143955     0.75   0.451    -.0173889    .0390969
       _cons |   .5068383   .0104559    48.47   0.000     .4863247    .5273519
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,577
                                                F(1, 1222)        =       0.68
                                                Prob > F          =     0.4085
                                                R-squared         =     0.0001
                                                Root MSE          =     .50001

                   (Std. err. adjusted for 1,223 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0117225    .014179    -0.83   0.409    -.0395403    .0160954
       _cons |   .4935263   .0098971    49.87   0.000     .4741092    .5129434
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 1211)        =       3.69
                                                Prob > F          =     0.0549
                                                R-squared         =     0.0007
                                                Root MSE          =     .49987

                   (Std. err. adjusted for 1,212 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   -.026435   .0137571    -1.92   0.055    -.0534253    .0005554
       _cons |    .525661    .009675    54.33   0.000     .5066793    .5446426
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,542
                                                F(1, 1207)        =       3.68
                                                Prob > F          =     0.0554
                                                R-squared         =     0.0007
                                                Root MSE          =     .49986

                   (Std. err. adjusted for 1,208 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0267704     .01396     1.92   0.055    -.0006181    .0541589
       _cons |   .4740157   .0098061    48.34   0.000     .4547769    .4932546
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,491
                                                F(1, 1208)        =       1.84
                                                Prob > F          =     0.1756
                                                R-squared         =     0.0004
                                                Root MSE          =      .5001

                   (Std. err. adjusted for 1,209 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0197342   .0145632    -1.36   0.176    -.0483061    .0088377
       _cons |   .5071429   .0100385    50.52   0.000      .487448    .5268377
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,629
                                                F(1, 1224)        =       1.81
                                                Prob > F          =     0.1784
                                                R-squared         =     0.0003
                                                Root MSE          =      .5001

                   (Std. err. adjusted for 1,225 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0185724   .0137941     1.35   0.178    -.0084903     .045635
       _cons |   .4930556   .0097674    50.48   0.000     .4738929    .5122182
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,459
                                                F(1, 1199)        =       0.13
                                                Prob > F          =     0.7221
                                                R-squared         =     0.0000
                                                Root MSE          =     .50013

                   (Std. err. adjusted for 1,200 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0053206   .0149564     0.36   0.722    -.0240231    .0346643
       _cons |   .4889976   .0103136    47.41   0.000     .4687628    .5092323
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,661
                                                F(1, 1218)        =       0.13
                                                Prob > F          =     0.7230
                                                R-squared         =     0.0000
                                                Root MSE          =     .50012

                   (Std. err. adjusted for 1,219 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0049028    .013828    -0.35   0.723    -.0320321    .0222266
       _cons |    .510158   .0095261    53.55   0.000     .4914687    .5288473
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,562
                                                F(1, 1215)        =       1.29
                                                Prob > F          =     0.2567
                                                R-squared         =     0.0003
                                                Root MSE          =     .49963

                   (Std. err. adjusted for 1,216 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0165674   .0146002    -1.13   0.257    -.0452119    .0120771
       _cons |   .4860031   .0103159    47.11   0.000     .4657642     .506242
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,558
                                                F(1, 1220)        =       1.21
                                                Prob > F          =     0.2710
                                                R-squared         =     0.0003
                                                Root MSE          =     .49963

                   (Std. err. adjusted for 1,221 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0161063   .0146243     1.10   0.271    -.0125852    .0447978
       _cons |   .5141732   .0104338    49.28   0.000     .4937031    .5346434
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,533
                                                F(1, 1201)        =       0.37
                                                Prob > F          =     0.5420
                                                R-squared         =     0.0001
                                                Root MSE          =     .49973

                   (Std. err. adjusted for 1,202 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0087083   .0142785    -0.61   0.542    -.0367218    .0193052
       _cons |     .48319   .0098813    48.90   0.000     .4638036    .5025764
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,587
                                                F(1, 1208)        =       0.30
                                                Prob > F          =     0.5864
                                                R-squared         =     0.0001
                                                Root MSE          =     .49975

                   (Std. err. adjusted for 1,209 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0075911   .0139472     0.54   0.586    -.0197723    .0349546
       _cons |   .5168363   .0099133    52.14   0.000     .4973871    .5362856
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,522
                                                F(1, 1204)        =       8.16
                                                Prob > F          =     0.0044
                                                R-squared         =     0.0017
                                                Root MSE          =     .49973

                   (Std. err. adjusted for 1,205 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0415509   .0145461    -2.86   0.004    -.0700893   -.0130124
       _cons |   .5139535   .0101379    50.70   0.000     .4940636    .5338434
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,598
                                                F(1, 1226)        =       7.88
                                                Prob > F          =     0.0051
                                                R-squared         =     0.0016
                                                Root MSE          =     .49976

                   (Std. err. adjusted for 1,227 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0397435   .0141618     2.81   0.005     .0119595    .0675275
       _cons |    .485782   .0103499    46.94   0.000     .4654765    .5060875
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,616
                                                F(1, 1231)        =       0.02
                                                Prob > F          =     0.8777
                                                R-squared         =     0.0000
                                                Root MSE          =     .50017

                   (Std. err. adjusted for 1,232 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0021468   .0139529    -0.15   0.878    -.0295209    .0252272
       _cons |   .5056433   .0096591    52.35   0.000     .4866932    .5245935
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,504
                                                F(1, 1227)        =       0.03
                                                Prob > F          =     0.8591
                                                R-squared         =     0.0000
                                                Root MSE          =     .50018

                   (Std. err. adjusted for 1,228 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0025886   .0145808     0.18   0.859    -.0260175    .0311947
       _cons |   .4938875   .0104694    47.17   0.000     .4733477    .5144273
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,075
                                                F(1, 1263)        =       7.40
                                                Prob > F          =     0.0066
                                                R-squared         =     0.0010
                                                Root MSE          =     .49992

                   (Std. err. adjusted for 1,264 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0308881   .0113578    -2.72   0.007    -.0531704   -.0086058
       _cons |   .5150919   .0079212    65.03   0.000     .4995517    .5306321
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,045
                                                F(1, 1109)        =       7.41
                                                Prob > F          =     0.0066
                                                R-squared         =     0.0022
                                                Root MSE          =      .4997

                   (Std. err. adjusted for 1,110 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0464724   .0170734     2.72   0.007     .0129727    .0799721
       _cons |   .4777132   .0117306    40.72   0.000     .4546965    .5007298
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,061
                                                F(1, 1260)        =       0.78
                                                Prob > F          =     0.3766
                                                R-squared         =     0.0001
                                                Root MSE          =     .50012

                   (Std. err. adjusted for 1,261 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |    .010113   .0114341     0.88   0.377    -.0123189    .0325449
       _cons |   .4993455    .008153    61.25   0.000     .4833506    .5153405
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,059
                                                F(1, 1120)        =       0.78
                                                Prob > F          =     0.3769
                                                R-squared         =     0.0002
                                                Root MSE          =     .50014

                   (Std. err. adjusted for 1,121 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0150368   .0170091    -0.88   0.377    -.0484101    .0183365
       _cons |   .5009728   .0121178    41.34   0.000     .4771966    .5247489
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,104
                                                F(1, 1294)        =       4.69
                                                Prob > F          =     0.0306
                                                R-squared         =     0.0002
                                                Root MSE          =     .50005

                   (Std. err. adjusted for 1,295 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |    .015572   .0071934     2.16   0.031       .00146     .029684
       _cons |   .4893204   .0050386    97.11   0.000     .4794357    .4992051
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,016
                                                F(1, 755)         =       4.81
                                                Prob > F          =     0.0285
                                                R-squared         =     0.0040
                                                Root MSE          =     .49934

                     (Std. err. adjusted for 756 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0635856   .0289809    -2.19   0.029    -.1204783   -.0066929
       _cons |   .5443548   .0208328    26.13   0.000     .5034577     .585252
------------------------------------------------------------------------------

. coefplot  age_candidate0  age_candidate1 gender_candidate0 gender_candidate1 class_candidate0 class_candidate1 rural_candidate0 r
> ural_candidate1 policy_welfare0 policy_welfare1 policy_immigration0 policy_immigration1 policy_environment0 policy_environment1 p
> olicy_abort0 policy_abort1   turnout_appeal1   conflict_appeal1   solidarity_appeal1, drop(_cons) eqrename(age_candidate0="30"  a
> ge_candidate1="60" gender_candidate0="Female" gender_candidate1="Male" class_candidate0="Middle class" class_candidate1="Working 
> class" rural_candidate0="Urban" rural_candidate1="Rural" policy_welfare1="Expand welfare state" policy_welfare0="Reduce taxes" po
> licy_immigration1="Restrict immigration" policy_immigration0="Liberal immigration" policy_environment0="Incease CO2 tax" policy_e
> nvironment1="Decrease CO2 tax" policy_abort0 ="No change in abortion" policy_abort1="More liberal abortion"  turnout_appeal1="Tur
> nout appeal" conflict_appeal1="Conflict appeal" solidarity_appeal1="Solidarity appeal") ylabel(, angle(0) nogrid) legend(off)  gr
> aphr(color(white))  xline(0, lpat(shortdash_dot)) plotregion(style(none)) color(black) ciopts(lpattern(line) lcolor(black)) ms(O)
>   scale(.8) nooffsets swap aseq title("Differences in marginal means by class", size(medsmall))
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)

. graph save "mm_diff_no.gph", replace
file mm_diff_no.gph saved

. 
. 
. clear 

. use  "Data_Britain.dta"

. 
. foreach x in $dimensions_uk class_appeal2{
  2.         reg selected i.`x'  if workingclass==0,  cl(RespondentSerial)
  3.         margins i.`x', post
  4.         est store `x'0
  5.         reg selected i.`x'  if workingclass==1,  cl(RespondentSerial)
  6.         margins i.`x', post
  7.         est store `x'1
  8. } 

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.12
                                                Prob > F          =     0.7273
                                                R-squared         =     0.0000
                                                Root MSE          =     .50012

                      (Std. err. adjusted for 962 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
     selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |  -.0057223   .0164023    -0.35   0.727    -.0379108    .0264663
        _cons |   .5027764   .0079582    63.18   0.000     .4871589    .5183939
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   .5027764   .0079582    63.18   0.000     .4871589    .5183939
          60  |   .4970541   .0084444    58.86   0.000     .4804825    .5136257
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       1.25
                                                Prob > F          =     0.2642
                                                R-squared         =     0.0003
                                                Root MSE          =     .50004

                    (Std. err. adjusted for 1,039 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
     selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |  -.0178106   .0159436    -1.12   0.264     -.049096    .0134747
        _cons |   .5087553    .007836    64.93   0.000     .4933791    .5241316
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   .5087553    .007836    64.93   0.000     .4933791    .5241316
          60  |   .4909447   .0081098    60.54   0.000     .4750312    .5068581
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.00
                                                Prob > F          =     0.9492
                                                R-squared         =     0.0000
                                                Root MSE          =     .50013

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |  -.0010395   .0163145    -0.06   0.949    -.0330557    .0309767
           _cons |   .5005203   .0081657    61.30   0.000     .4844955     .516545
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   .5005203   .0081657    61.30   0.000     .4844955     .516545
           Male  |   .4994808   .0081488    61.30   0.000     .4834893    .5154723
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      11.44
                                                Prob > F          =     0.0007
                                                R-squared         =     0.0026
                                                Root MSE          =     .49947

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |  -.0510129   .0150837    -3.38   0.001    -.0806109   -.0214149
           _cons |   .5256783   .0075988    69.18   0.000     .5107675    .5405891
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   .5256783   .0075988    69.18   0.000     .5107675    .5405891
           Male  |   .4746654   .0075057    63.24   0.000     .4599373    .4893935
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       1.76
                                                Prob > F          =     0.1852
                                                R-squared         =     0.0005
                                                Root MSE          =     .50001

                        (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
       selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |  -.0218295   .0164645    -1.33   0.185      -.05414    .0104809
          _cons |   .5109034   .0082248    62.12   0.000     .4947628    .5270441
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   .5109034   .0082248    62.12   0.000     .4947628    .5270441
 Working class  |   .4890739   .0082433    59.33   0.000     .4728969    .5052509
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      12.79
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0031
                                                Root MSE          =     .49934

                      (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
       selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |   .0558664   .0156239     3.58   0.000     .0252083    .0865245
          _cons |   .4728464   .0075974    62.24   0.000     .4579385    .4877544
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   .4728464   .0075974    62.24   0.000     .4579385    .4877544
 Working class  |   .5287129   .0080499    65.68   0.000     .5129169    .5445089
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(0, 961)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0000
                                                Root MSE          =     .50013

                        (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
       selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |          0   .0167957     0.00   1.000    -.0329605    .0329605
          _cons |         .5   .0085725    58.33   0.000     .4831771    .5168229
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |         .5   .0085725    58.33   0.000     .4831771    .5168229
         Rural  |         .5   .0082233    60.80   0.000     .4838624    .5161376
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       0.14
                                                Prob > F          =     0.7106
                                                R-squared         =     0.0000
                                                Root MSE          =     .50011

                      (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
       selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |  -.0057793   .0155698    -0.37   0.711    -.0363312    .0247726
          _cons |   .5029703   .0080048    62.83   0.000     .4872628    .5186778
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   .5029703   .0080048    62.83   0.000     .4872628    .5186778
         Rural  |    .497191   .0075653    65.72   0.000      .482346     .512036
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       6.05
                                                Prob > F          =     0.0141
                                                R-squared         =     0.0017
                                                Root MSE          =      .4997

                              (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |     .04158   .0169117     2.46   0.014      .008392    .0747681
                _cons |     .47921   .0084658    56.61   0.000     .4625964    .4958236
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |     .47921   .0084658    56.61   0.000     .4625964    .4958236
Expand welfare state  |     .52079   .0084592    61.57   0.000     .5041895    .5373906
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      10.19
                                                Prob > F          =     0.0015
                                                R-squared         =     0.0026
                                                Root MSE          =     .49948

                            (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |   .0505345   .0158292     3.19   0.001     .0194737    .0815953
                _cons |   .4744774   .0080115    59.22   0.000     .4587569    .4901979
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   .4744774   .0080115    59.22   0.000     .4587569    .4901979
Expand welfare state  |   .5250119    .007837    66.99   0.000     .5096338      .54039
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       8.84
                                                Prob > F          =     0.0030
                                                R-squared         =     0.0024
                                                Root MSE          =     .49952

                              (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |    .049377   .0166076     2.97   0.003     .0167855    .0819684
                _cons |   .4752217   .0083594    56.85   0.000     .4588168    .4916266
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   .4752217   .0083594    56.85   0.000     .4588168    .4916266
Restrict immigration  |   .5245987    .008268    63.45   0.000     .5083732    .5408241
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      46.71
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0123
                                                Root MSE          =     .49705

                            (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .1106835   .0161944     6.83   0.000     .0789059     .142461
                _cons |   .4447115    .008125    54.73   0.000     .4287682    .4606549
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   .4447115    .008125    54.73   0.000     .4287682    .4606549
Restrict immigration  |    .555395   .0081622    68.04   0.000     .5393786    .5714114
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      42.11
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0123
                                                Root MSE          =     .49706

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
Prioritize growth  |  -.1107201   .0170615    -6.49   0.000    -.1442022   -.0772379
             _cons |   .5547558   .0084691    65.50   0.000     .5381357    .5713759
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
 policy_environment |
Prioritize climate  |   .5547558   .0084691    65.50   0.000     .5381357    .5713759
 Prioritize growth  |   .4440357   .0086834    51.14   0.000     .4269952    .4610763
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      15.66
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0040
                                                Root MSE          =     .49913

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
Prioritize growth  |  -.0630573   .0159333    -3.96   0.000    -.0943223   -.0317922
             _cons |   .5320293   .0080937    65.73   0.000     .5161474    .5479113
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
 policy_environment |
Prioritize climate  |   .5320293   .0080937    65.73   0.000     .5161474    .5479113
 Prioritize growth  |   .4689721   .0078696    59.59   0.000     .4535299    .4844142
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      12.61
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0036
                                                Root MSE          =     .49922

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
EU independence  |  -.0602989   .0169829    -3.55   0.000    -.0936268   -.0269711
           _cons |   .5298047   .0084102    63.00   0.000     .5133002    .5463092
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
 EU cooperation  |   .5298047   .0084102    63.00   0.000     .5133002    .5463092
EU independence  |   .4695058   .0085989    54.60   0.000     .4526309    .4863806
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       1.34
                                                Prob > F          =     0.2468
                                                R-squared         =     0.0004
                                                Root MSE          =     .50003

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
EU independence  |  -.0187683   .0161978    -1.16   0.247    -.0505525     .013016
           _cons |   .5094157    .008133    62.64   0.000     .4934568    .5253747
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
 EU cooperation  |   .5094157    .008133    62.64   0.000     .4934568    .5253747
EU independence  |   .4906475   .0080675    60.82   0.000     .4748171    .5064779
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.95
                                                Prob > F          =     0.3309
                                                R-squared         =     0.0003
                                                Root MSE          =     .50006

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |   -.016799   .0172683    -0.97   0.331     -.050687     .017089
           _cons |   .5067013    .006888    73.56   0.000     .4931839    .5202186
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   .5067013    .006888    73.56   0.000     .4931839    .5202186
   Conflict appeal  |   .4899023   .0103825    47.19   0.000     .4695274    .5102772
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       8.20
                                                Prob > F          =     0.0043
                                                R-squared         =     0.0020
                                                Root MSE          =     .49961

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
        selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |   .0462539   .0161552     2.86   0.004     .0145533    .0779545
           _cons |   .4816587   .0064174    75.05   0.000     .4690661    .4942513
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   .4816587   .0064174    75.05   0.000     .4690661    .4942513
   Conflict appeal  |   .5279126   .0097535    54.13   0.000     .5087737    .5470515
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       2.27
                                                Prob > F          =     0.1326
                                                R-squared         =     0.0006
                                                Root MSE          =     .49997

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |  -.0259387   .0172315    -1.51   0.133    -.0597545    .0078771
             _cons |   .5104348   .0069353    73.60   0.000     .4968246    .5240449
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   .5104348   .0069353    73.60   0.000     .4968246    .5240449
   Solidarity appeal  |   .4844961   .0103012    47.03   0.000     .4642807    .5047116
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       4.07
                                                Prob > F          =     0.0439
                                                R-squared         =     0.0011
                                                Root MSE          =     .49985

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |  -.0335819   .0166472    -2.02   0.044     -.066248   -.0009159
             _cons |   .5134376   .0066726    76.95   0.000     .5003444    .5265309
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   .5134376   .0066726    76.95   0.000     .5003444    .5265309
   Solidarity appeal  |   .4798557   .0099827    48.07   0.000     .4602672    .4994442
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(2, 961)         =       4.87
                                                Prob > F          =     0.0079
                                                R-squared         =     0.0027
                                                Root MSE          =     .49953

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |  -.0617317   .0228296    -2.70   0.007    -.1065333   -.0169301
Solidarity appeal  |  -.0671379   .0227834    -2.95   0.003    -.1118488   -.0224269
                   |
             _cons |    .551634   .0166082    33.21   0.000     .5190415    .5842264
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |    .551634   .0166082    33.21   0.000     .5190415    .5842264
  Conflict appeal  |   .4899023   .0103838    47.18   0.000     .4695247    .5102799
Solidarity appeal  |   .4844961   .0103025    47.03   0.000     .4642781    .5047142
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(2, 1038)        =       4.11
                                                Prob > F          =     0.0167
                                                R-squared         =     0.0021
                                                Root MSE          =     .49966

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |   .0427055   .0216141     1.98   0.048     .0002932    .0851179
Solidarity appeal  |  -.0053514   .0222704    -0.24   0.810    -.0490516    .0383488
                   |
             _cons |   .4852071   .0159692    30.38   0.000     .4538715    .5165427
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   .4852071   .0159692    30.38   0.000     .4538715    .5165427
  Conflict appeal  |   .5279126   .0097547    54.12   0.000     .5087714    .5470538
Solidarity appeal  |   .4798557   .0099839    48.06   0.000     .4602648    .4994465
------------------------------------------------------------------------------------

.  
. 
. coefplot  (age_candidate0    gender_candidate0 class_candidate0  rural_candidate0  policy_welfare0  policy_immigration0  policy_e
> nvironment0  policy_eu0    class_appeal20, label(Middle class) color(black))  (age_candidate1    gender_candidate1 class_candidat
> e1  rural_candidate1  policy_welfare1  policy_immigration1  policy_environment1  policy_eu1   class_appeal21, label(Working class
> )), drop(_cons)  legend(off)  xline(.5, lpat(shortdash_dot)) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Margin
> al means by class", size(medsmall))  

. graph save "mm_class_uk.gph", replace
file mm_class_uk.gph saved

. 
. 
. foreach x in $dimensions_uk turnout_appeal{
  2.         reg selected workingclass if `x'==0, cl(RespondentSerial)
  3.         est store `x'0
  4.         reg selected workingclass if `x'==1, cl(RespondentSerial)
  5.         est store `x'1
  6. }

Linear regression                               Number of obs     =      4,094
                                                F(1, 1889)        =       0.29
                                                Prob > F          =     0.5924
                                                R-squared         =     0.0000
                                                Root MSE          =     .50008

                   (Std. err. adjusted for 1,890 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0059789   .0111659     0.54   0.592    -.0159198    .0278777
       _cons |   .5027764   .0079561    63.19   0.000     .4871727    .5183801
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,910
                                                F(1, 1859)        =       0.27
                                                Prob > F          =     0.6018
                                                R-squared         =     0.0000
                                                Root MSE          =     .50008

                   (Std. err. adjusted for 1,860 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0061094   .0117053    -0.52   0.602    -.0290663    .0168475
       _cons |   .4970541   .0084423    58.88   0.000     .4804968    .5136114
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,986
                                                F(1, 1865)        =       5.09
                                                Prob > F          =     0.0242
                                                R-squared         =     0.0006
                                                Root MSE          =     .49978

                   (Std. err. adjusted for 1,866 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |    .025158   .0111518     2.26   0.024     .0032866    .0470294
       _cons |   .5005203   .0081637    61.31   0.000     .4845094    .5165311
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,018
                                                F(1, 1881)        =       5.02
                                                Prob > F          =     0.0252
                                                R-squared         =     0.0006
                                                Root MSE          =     .49979

                   (Std. err. adjusted for 1,882 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0248154   .0110761    -2.24   0.025    -.0465382   -.0030926
       _cons |   .4994808   .0081467    61.31   0.000     .4835033    .5154582
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,062
                                                F(1, 1886)        =      11.56
                                                Prob > F          =     0.0007
                                                R-squared         =     0.0014
                                                Root MSE          =     .49968

                   (Std. err. adjusted for 1,887 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   -.038057   .0111941    -3.40   0.001    -.0600111   -.0161029
       _cons |   .5109034   .0082227    62.13   0.000      .494777    .5270299
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,942
                                                F(1, 1876)        =      11.84
                                                Prob > F          =     0.0006
                                                R-squared         =     0.0016
                                                Root MSE          =     .49965

                   (Std. err. adjusted for 1,877 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |    .039639   .0115192     3.44   0.001     .0170472    .0622308
       _cons |   .4890739   .0082412    59.34   0.000      .472911    .5052368
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,904
                                                F(1, 1854)        =       0.06
                                                Prob > F          =     0.8001
                                                R-squared         =     0.0000
                                                Root MSE          =     .50012

                   (Std. err. adjusted for 1,855 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0029703   .0117261     0.25   0.800    -.0200274     .025968
       _cons |         .5   .0085703    58.34   0.000     .4831916    .5168084
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,100
                                                F(1, 1871)        =       0.06
                                                Prob > F          =     0.8015
                                                R-squared         =     0.0000
                                                Root MSE          =     .50012

                   (Std. err. adjusted for 1,872 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   -.002809   .0111712    -0.25   0.801    -.0247183    .0191004
       _cons |         .5   .0082211    60.82   0.000     .4838765    .5161235
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,981
                                                F(1, 1873)        =       0.16
                                                Prob > F          =     0.6847
                                                R-squared         =     0.0000
                                                Root MSE          =     .49958

                   (Std. err. adjusted for 1,874 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0047326   .0116529    -0.41   0.685    -.0275866    .0181214
       _cons |     .47921   .0084636    56.62   0.000     .4626108    .4958091
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,023
                                                F(1, 1872)        =       0.13
                                                Prob > F          =     0.7143
                                                R-squared         =     0.0000
                                                Root MSE          =     .49959

                   (Std. err. adjusted for 1,873 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0042219   .0115288     0.37   0.714    -.0183887    .0268325
       _cons |     .52079    .008457    61.58   0.000     .5042039    .5373761
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,997
                                                F(1, 1882)        =       6.85
                                                Prob > F          =     0.0089
                                                R-squared         =     0.0009
                                                Root MSE          =     .49824

                   (Std. err. adjusted for 1,883 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0305102   .0116547    -2.62   0.009    -.0533677   -.0076527
       _cons |   .4752217   .0083573    56.86   0.000     .4588312    .4916122
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,007
                                                F(1, 1854)        =       7.03
                                                Prob > F          =     0.0081
                                                R-squared         =     0.0010
                                                Root MSE          =     .49824

                   (Std. err. adjusted for 1,855 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0307963   .0116155     2.65   0.008     .0080155    .0535772
       _cons |   .5245987   .0082659    63.47   0.000     .5083872    .5408101
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,990
                                                F(1, 1880)        =       3.77
                                                Prob > F          =     0.0525
                                                R-squared         =     0.0005
                                                Root MSE          =     .49813

                   (Std. err. adjusted for 1,881 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0227264   .0117119    -1.94   0.052    -.0456962    .0002433
       _cons |   .5547558   .0084669    65.52   0.000     .5381502    .5713613
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,014
                                                F(1, 1870)        =       4.53
                                                Prob > F          =     0.0334
                                                R-squared         =     0.0006
                                                Root MSE          =     .49813

                   (Std. err. adjusted for 1,871 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0249363   .0117161     2.13   0.033     .0019583    .0479143
       _cons |   .4440357   .0086811    51.15   0.000       .42701    .4610614
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,017
                                                F(1, 1890)        =       3.04
                                                Prob > F          =     0.0815
                                                R-squared         =     0.0004
                                                Root MSE          =     .49965

                   (Std. err. adjusted for 1,891 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   -.020389   .0116967    -1.74   0.081    -.0433287    .0025508
       _cons |   .5298047    .008408    63.01   0.000     .5133147    .5462947
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,987
                                                F(1, 1879)        =       3.22
                                                Prob > F          =     0.0731
                                                R-squared         =     0.0004
                                                Root MSE          =     .49964

                   (Std. err. adjusted for 1,880 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0211417   .0117881     1.79   0.073    -.0019775    .0442609
       _cons |   .4695058   .0085967    54.61   0.000     .4526457    .4863659
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,821
                                                F(1, 1941)        =       7.08
                                                Prob > F          =     0.0079
                                                R-squared         =     0.0006
                                                Root MSE          =     .49991

                   (Std. err. adjusted for 1,942 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0250426   .0094118    -2.66   0.008    -.0435008   -.0065843
       _cons |   .5067013    .006886    73.58   0.000     .4931965    .5202061
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,183
                                                F(1, 1746)        =       7.12
                                                Prob > F          =     0.0077
                                                R-squared         =     0.0014
                                                Root MSE          =      .4997

                   (Std. err. adjusted for 1,747 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0380103   .0142426     2.67   0.008      .010076    .0659447
       _cons |   .4899023   .0103803    47.20   0.000     .4695431    .5102615
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,793
                                                F(1, 1952)        =       0.10
                                                Prob > F          =     0.7550
                                                R-squared         =     0.0000
                                                Root MSE          =     .49996

                   (Std. err. adjusted for 1,953 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0030028   .0096215     0.31   0.755    -.0158666    .0218723
       _cons |   .5104348   .0069333    73.62   0.000     .4968373    .5240323
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,211
                                                F(1, 1738)        =       0.10
                                                Prob > F          =     0.7463
                                                R-squared         =     0.0000
                                                Root MSE          =     .49983

                   (Std. err. adjusted for 1,739 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0046404    .014342    -0.32   0.746    -.0327698    .0234889
       _cons |   .4844961   .0102991    47.04   0.000     .4642963     .504696
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      6,394
                                                F(1, 1997)        =       8.10
                                                Prob > F          =     0.0045
                                                R-squared         =     0.0003
                                                Root MSE          =     .49999

                   (Std. err. adjusted for 1,998 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |   .0165875   .0058288     2.85   0.004     .0051564    .0280186
       _cons |   .4871878   .0041644   116.99   0.000     .4790208    .4953548
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,610
                                                F(1, 1185)        =       8.31
                                                Prob > F          =     0.0040
                                                R-squared         =     0.0044
                                                Root MSE          =     .49893

                   (Std. err. adjusted for 1,186 clusters in RespondentSerial)
------------------------------------------------------------------------------
             |               Robust
    selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
workingclass |  -.0664269   .0230397    -2.88   0.004      -.11163   -.0212238
       _cons |    .551634   .0166074    33.22   0.000     .5190509    .5842171
------------------------------------------------------------------------------

. coefplot  age_candidate0  age_candidate1 gender_candidate0 gender_candidate1 class_candidate0 class_candidate1 rural_candidate0 r
> ural_candidate1 policy_welfare0 policy_welfare1 policy_immigration0 policy_immigration1 policy_environment0 policy_environment1 p
> olicy_eu0 policy_eu1   turnout_appeal1   conflict_appeal1   solidarity_appeal1, drop(_cons) eqrename(age_candidate0="30"  age_can
> didate1="60" gender_candidate0="Female" gender_candidate1="Male" class_candidate0="Middle class" class_candidate1="Working class"
>  rural_candidate0="Urban" rural_candidate1="Rural" policy_welfare1="Expand welfare state" policy_welfare0="Reduce taxes" policy_i
> mmigration0="Liberal immigration" policy_immigration1="Restrict immigration"policy_environment0="Prioritize climate" policy_envir
> onment1="Prioritize growth" policy_eu0="EU coorporation" policy_eu1 ="EU independence" turnout_appeal1="Turnout appeal" conflict_
> appeal1="Conflict appeal" solidarity_appeal1="Solidarity appeal") ylabel(, angle(0) nogrid) legend(off)  graphr(color(white))  xl
> ine(0, lpat(shortdash_dot)) plotregion(style(none)) color(black) ciopts(lpattern(line) lcolor(black)) ms(O) scale(.8) nooffsets s
> wap aseq title("Differences in marginal means by class", size(medsmall))
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)

. graph save "mm_diff_uk.gph", replace
file mm_diff_uk.gph saved

. 
. 
. gr combine "mm_class_no.gph" "mm_diff_no.gph",   imargin(small)   title("Norway", size(small))
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)

. gr save "mainmm_no.gph", replace
file mainmm_no.gph saved

. gr combine "mm_class_uk.gph" "mm_diff_uk.gph",   imargin(small)   title("Britain", size(small))
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)

. gr save "mainmm_uk.gph", replace
file mainmm_uk.gph saved

. grc1leg2 "mainmm_no.gph" "mainmm_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)
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(note:  named style line not found in class linepattern, default attributes used)

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA2.gph", replace
file figureA2.gph saved

. gr export  "figureA2.pdf",as(pdf) replace
file figureA2.pdf saved as PDF format

. 
. 
. 
. ********************************************************************************
. *FIGURE A3**********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg rightrating $dimensions_interactions_no workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      5,167
                                                F(21, 1306)       =      44.93
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1678
                                                Root MSE          =     1.1875

                            (Std. err. adjusted for 1,307 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |   .0824923   .0446495     1.85   0.065    -.0051004     .170085
     gender_candidate |   .0749511    .044695     1.68   0.094    -.0127308     .162633
      class_candidate |  -.1047124    .045227    -2.32   0.021    -.1934379   -.0159869
      rural_candidate |  -.0804114   .0462768    -1.74   0.083    -.1711964    .0103736
       policy_welfare |  -.8783772   .0509737   -17.23   0.000    -.9783765    -.778378
   policy_immigration |   .6843187   .0511503    13.38   0.000     .5839731    .7846644
   policy_environment |   .3381674   .0459082     7.37   0.000     .2481056    .4282292
         policy_abort |  -.2716288   .0448239    -6.06   0.000    -.3595634   -.1836941
      conflict_appeal |  -.3273727   .0595307    -5.50   0.000    -.4441589   -.2105866
    solidarity_appeal |   -.058465   .0598191    -0.98   0.329    -.1758171    .0588871
     age_candidateXwc |  -.0120817   .0667192    -0.18   0.856    -.1429701    .1188068
  gender_candidateXwc |  -.0701551   .0655749    -1.07   0.285    -.1987988    .0584886
   class_candidateXwc |  -.0161018   .0679073    -0.24   0.813    -.1493211    .1171176
   rural_candidateXwc |  -.0581682   .0666942    -0.87   0.383    -.1890077    .0726713
    policy_welfareXwc |   .4170011   .0754979     5.52   0.000     .2688907    .5651115
policy_immigrationXwc |  -.0379411   .0736602    -0.52   0.607    -.1824463    .1065642
policy_environmentXwc |  -.1762782   .0678499    -2.60   0.009     -.309385   -.0431714
      policy_abortXwc |   .2105154   .0667611     3.15   0.002     .0795446    .3414863
   conflict_appealXwc |   .0643339   .0907933     0.71   0.479    -.1137828    .2424507
 solidarity_appealXwc |   .0443401   .0898907     0.49   0.622    -.1320058     .220686
         workingclass |  -.0638084   .1153332    -0.55   0.580     -.290067    .1624503
                _cons |   4.165507   .0807533    51.58   0.000     4.007087    4.323927
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_no workingclass) ylabel(, nogrid)  xline(0, lpat(shortdash_dot)) legend(off) plotregion
> (style(none)) lwidth(vvthin) ms(O) scale(.8) title("Rightscale: Differences in AMCE by class", size(medsmall)) 

. graph save "amce_rightrating_diff_no.gph", replace
file amce_rightrating_diff_no.gph saved

. 
. clear 

. use  "Data_Britain.dta"

. 
. reg rightrating $dimensions_interactions_uk workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(21, 2000)       =      23.43
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0765
                                                Root MSE          =     1.3497

                            (Std. err. adjusted for 2,001 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |   .0350976   .0435549     0.81   0.420    -.0503202    .1205154
     gender_candidate |   .0425821   .0412745     1.03   0.302    -.0383634    .1235276
      class_candidate |  -.1932184   .0441232    -4.38   0.000    -.2797506   -.1066861
      rural_candidate |     .03958   .0431551     0.92   0.359    -.0450536    .1242136
       policy_welfare |  -.4506763   .0461704    -9.76   0.000    -.5412234   -.3601291
   policy_immigration |   .5300875   .0472969    11.21   0.000     .4373312    .6228438
   policy_environment |   .3280157   .0442713     7.41   0.000      .241193    .4148383
            policy_eu |   .2335652   .0431422     5.41   0.000     .1489569    .3181734
      conflict_appeal |   -.315522   .0624509    -5.05   0.000    -.4379975   -.1930464
    solidarity_appeal |  -.1083193   .0580335    -1.87   0.062    -.2221317    .0054932
     age_candidateXwc |  -.0872629   .0594459    -1.47   0.142    -.2038453    .0293194
  gender_candidateXwc |   .0307148    .059447     0.52   0.605    -.0858697    .1472993
   class_candidateXwc |  -.0036836   .0626946    -0.06   0.953    -.1266373      .11927
   rural_candidateXwc |   .0456845   .0604747     0.76   0.450    -.0729155    .1642844
    policy_welfareXwc |   .1566595   .0659331     2.38   0.018     .0273548    .2859642
policy_immigrationXwc |  -.0463444   .0662504    -0.70   0.484    -.1762715    .0835826
policy_environmentXwc |   -.183271   .0631922    -2.90   0.004    -.3072004   -.0593416
         policy_euXwc |  -.0472449   .0617566    -0.77   0.444     -.168359    .0738691
   conflict_appealXwc |   .0504976   .0866775     0.58   0.560    -.1194901    .2204853
 solidarity_appealXwc |  -.0441884   .0839857    -0.53   0.599     -.208897    .1205201
         workingclass |   .0545873   .1121264     0.49   0.626    -.1653095    .2744841
                _cons |   3.954819   .0835829    47.32   0.000       3.7909    4.118737
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_uk workingclass) ylabel(, nogrid)  xline(0, lpat(shortdash_dot)) legend(off) plotregion
> (style(none)) lwidth(vvthin) ms(O) scale(.8) title("Rightscale: Differences in AMCE by class", size(medsmall)) 

. graph save "amce_rightrating_diff_uk.gph", replace
file amce_rightrating_diff_uk.gph saved

. 
. 
. gr combine "amce_rightrating_class_no.gph"  "amce_rightrating_diff_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "rightrating_no.gph", replace
file rightrating_no.gph saved

. gr combine "amce_rightrating_class_uk.gph"  "amce_rightrating_diff_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "rightrating_uk.gph", replace
file rightrating_uk.gph saved

. grc1leg2 "rightrating_no.gph" "rightrating_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA3.gph", replace
file figureA3.gph saved

. gr export  "figureA3.pdf",as(pdf) replace
file figureA3.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *FIGURE A4**********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg represented $dimensions_interactions_no workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      5,189
                                                F(21, 1307)       =       3.50
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0158
                                                Root MSE          =     1.4108

                            (Std. err. adjusted for 1,308 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |   .0105508   .0562064     0.19   0.851    -.0997138    .1208154
     gender_candidate |  -.0065184   .0535887    -0.12   0.903    -.1116476    .0986109
      class_candidate |  -.1378516   .0529066    -2.61   0.009    -.2416427   -.0340605
      rural_candidate |  -.0677817   .0554203    -1.22   0.222    -.1765041    .0409408
       policy_welfare |   .2792299   .0616835     4.53   0.000     .1582204    .4002394
   policy_immigration |  -.0073837   .0606669    -0.12   0.903    -.1263988    .1116314
   policy_environment |   .0637773   .0565208     1.13   0.259    -.0471041    .1746587
         policy_abort |  -.0665643   .0574726    -1.16   0.247     -.179313    .0461845
      conflict_appeal |  -.1566237   .0767447    -2.04   0.041      -.30718   -.0060674
    solidarity_appeal |    -.05972   .0740777    -0.81   0.420    -.2050442    .0856042
     age_candidateXwc |  -.1010382   .0808131    -1.25   0.211    -.2595759    .0574994
  gender_candidateXwc |  -.0608642   .0784559    -0.78   0.438    -.2147775    .0930492
   class_candidateXwc |   .1351736   .0782403     1.73   0.084    -.0183168     .288664
   rural_candidateXwc |   .0213527   .0792978     0.27   0.788    -.1342123    .1769176
    policy_welfareXwc |  -.1104762   .0892008    -1.24   0.216    -.2854686    .0645162
policy_immigrationXwc |   .1886858   .0903001     2.09   0.037     .0115367    .3658348
policy_environmentXwc |    .083651   .0819743     1.02   0.308    -.0771646    .2444666
      policy_abortXwc |   .0315217   .0853778     0.37   0.712    -.1359708    .1990143
   conflict_appealXwc |   .1889989     .11216     1.69   0.092    -.0310343    .4090322
 solidarity_appealXwc |  -.0379946   .1076158    -0.35   0.724    -.2491133     .173124
         workingclass |  -.0767396   .1496449    -0.51   0.608    -.3703102    .2168309
                _cons |   3.587907   .1056976    33.95   0.000     3.380552    3.795263
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_no workingclass) ylabel(, nogrid)  xline(0, lpat(shortdash_dot)) legend(off) plotregion
> (style(none)) lwidth(vvthin) ms(O) scale(.8) title("Represented: Differences in AMCE by class", size(medsmall)) 

. graph save "amce_represented_diff_no.gph", replace
file amce_represented_diff_no.gph saved

. 
. clear 

. use  "Data_Britain.dta"

. 
. reg represented $dimensions_interactions_uk workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(21, 2000)       =       6.47
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0197
                                                Root MSE          =     1.5139

                            (Std. err. adjusted for 2,001 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |   .0066087   .0491538     0.13   0.893    -.0897892    .1030067
     gender_candidate |   .0197974   .0499866     0.40   0.692    -.0782338    .1178287
      class_candidate |  -.1142874   .0480765    -2.38   0.018    -.2085726   -.0200022
      rural_candidate |   .0293545   .0481686     0.61   0.542    -.0651115    .1238204
       policy_welfare |   .1735569   .0535563     3.24   0.001     .0685249    .2785889
   policy_immigration |   .0338651   .0549928     0.62   0.538    -.0739841    .1417143
   policy_environment |  -.2599986   .0508194    -5.12   0.000    -.3596632   -.1603341
            policy_eu |  -.1378337   .0504666    -2.73   0.006    -.2368064    -.038861
      conflict_appeal |  -.0517714   .0697064    -0.74   0.458    -.1884762    .0849333
    solidarity_appeal |  -.0731968   .0679876    -1.08   0.282    -.2065307    .0601372
     age_candidateXwc |  -.0186638   .0688906    -0.27   0.786    -.1537686    .1164411
  gender_candidateXwc |  -.1030838   .0690058    -1.49   0.135    -.2384147    .0322471
   class_candidateXwc |   .3139696   .0682994     4.60   0.000     .1800242    .4479151
   rural_candidateXwc |   .0196582    .068219     0.29   0.773    -.1141296     .153446
    policy_welfareXwc |  -.0591429   .0748463    -0.79   0.430    -.2059277    .0876419
policy_immigrationXwc |   .2202464   .0763517     2.88   0.004     .0705091    .3699837
policy_environmentXwc |   .0396781   .0709492     0.56   0.576    -.0994639    .1788202
         policy_euXwc |    .131445   .0723184     1.82   0.069    -.0103823    .2732723
   conflict_appealXwc |   .1178936   .0976808     1.21   0.228    -.0736731    .3094604
 solidarity_appealXwc |    .109427   .0979737     1.12   0.264    -.0827142    .3015683
         workingclass |  -.1979541   .1309806    -1.51   0.131    -.4548268    .0589186
                _cons |   3.782728   .0934778    40.47   0.000     3.599404    3.966052
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_uk workingclass) ylabel(, nogrid) xsc(r(-.4 .6)) xlab(-.4 (.2) .6) xline(0, lpat(shortd
> ash_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Represented: Differences in AMCE by class", s
> ize(medsmall)) 

. graph save "amce_represented_diff_uk.gph", replace
file amce_represented_diff_uk.gph saved

. 
. gr combine "amce_represented_class_no.gph"  "amce_represented_diff_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "represented_no.gph", replace
file represented_no.gph saved

. gr combine "amce_represented_class_uk.gph"  "amce_represented_diff_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "represented_uk.gph", replace
file represented_uk.gph saved

. grc1leg2 "represented_no.gph" "represented_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA4.gph", replace
file figureA4.gph saved

. gr export  "figureA4.pdf",as(pdf) replace
file figureA4.pdf saved as PDF format

. 
. 
. 
. ********************************************************************************
. *FIGURE A5**********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. foreach x in $dimensions_no class_appeal2{
  2.         reg rightrating i.`x'  if workingclass==0,  cl(RespondentSerial)
  3.         margins i.`x', post
  4.         est store `x'0
  5.         reg rightrating i.`x'  if workingclass==1,  cl(RespondentSerial)
  6.         margins i.`x', post
  7.         est store `x'1
  8. } 

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =       2.96
                                                Prob > F          =     0.0857
                                                R-squared         =     0.0012
                                                Root MSE          =     1.2943

                      (Std. err. adjusted for 654 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
  rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |   .0892956   .0518823     1.72   0.086    -.0125807    .1911718
        _cons |   3.897211   .0353591   110.22   0.000      3.82778    3.966642
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   3.897211   .0353591   110.22   0.000      3.82778    3.966642
          60  |   3.986507   .0321258   124.09   0.000     3.923424    4.049589
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =       1.99
                                                Prob > F          =     0.1587
                                                R-squared         =     0.0008
                                                Root MSE          =     1.3013

                      (Std. err. adjusted for 653 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
  rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |   .0739829   .0524358     1.41   0.159    -.0289804    .1769463
        _cons |   4.008507   .0344478   116.36   0.000     3.940865    4.076149
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   4.008507   .0344478   116.36   0.000     3.940865    4.076149
          60  |    4.08249    .035064   116.43   0.000     4.013638    4.151342
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =       1.29
                                                Prob > F          =     0.2563
                                                R-squared         =     0.0005
                                                Root MSE          =     1.2947

                         (Std. err. adjusted for 654 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |   .0572814   .0504125     1.14   0.256    -.0417088    .1562716
           _cons |   3.914747    .032806   119.33   0.000     3.850329    3.979164
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   3.914747    .032806   119.33   0.000     3.850329    3.979164
           Male  |   3.972028   .0334646   118.69   0.000     3.906317    4.037739
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =       0.01
                                                Prob > F          =     0.9183
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3018

                         (Std. err. adjusted for 653 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |  -.0052193   .0508714    -0.10   0.918    -.1051108    .0946721
           _cons |   4.047988   .0335241   120.75   0.000     3.982159    4.113816
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   4.047988   .0335241   120.75   0.000     3.982159    4.113816
           Male  |   4.042768   .0347828   116.23   0.000     3.974468    4.111068
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =       2.68
                                                Prob > F          =     0.1022
                                                R-squared         =     0.0011
                                                Root MSE          =     1.2943

                        (Std. err. adjusted for 654 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |  -.0845723   .0516777    -1.64   0.102    -.1860469    .0169023
          _cons |   3.985948   .0340735   116.98   0.000     3.919042    4.052855
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   3.985948   .0340735   116.98   0.000     3.919042    4.052855
 Working class  |   3.901376   .0332075   117.48   0.000      3.83617    3.966582
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =       6.16
                                                Prob > F          =     0.0133
                                                R-squared         =     0.0026
                                                Root MSE          =     1.3002

                        (Std. err. adjusted for 653 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |  -.1321991   .0532768    -2.48   0.013    -.2368139   -.0275843
          _cons |   4.114355   .0345796   118.98   0.000     4.046454    4.182256
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   4.114355   .0345796   118.98   0.000     4.046454    4.182256
 Working class  |   3.982156   .0355091   112.14   0.000      3.91243    4.051882
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =       3.04
                                                Prob > F          =     0.0819
                                                R-squared         =     0.0012
                                                Root MSE          =     1.2942

                        (Std. err. adjusted for 654 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |  -.0903967   .0518712    -1.74   0.082    -.1922511    .0114577
          _cons |   3.990323   .0340185   117.30   0.000     3.923524    4.057121
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   3.990323   .0340185   117.30   0.000     3.923524    4.057121
         Rural  |   3.899926   .0334395   116.63   0.000     3.834264    3.965588
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =       7.41
                                                Prob > F          =     0.0067
                                                R-squared         =     0.0028
                                                Root MSE          =        1.3

                        (Std. err. adjusted for 653 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |   -.137549   .0505306    -2.72   0.007    -.2367714   -.0383266
          _cons |   4.116559   .0324146   127.00   0.000      4.05291    4.180209
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   4.116559   .0324146   127.00   0.000      4.05291    4.180209
         Rural  |    3.97901   .0353794   112.47   0.000     3.909539    4.048482
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =     250.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1059
                                                Root MSE          =     1.2246

                              (Std. err. adjusted for 654 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |  -.8424265   .0532728   -15.81   0.000    -.9470333   -.7378198
                _cons |   4.360367   .0340943   127.89   0.000      4.29342    4.427315
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   4.360367   .0340943   127.89   0.000      4.29342    4.427315
Expand welfare state  |   3.517941   .0339416   103.65   0.000     3.451293    3.584589
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =      61.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0305
                                                Root MSE          =     1.2818

                              (Std. err. adjusted for 653 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |  -.4543465   .0578047    -7.86   0.000    -.5678524   -.3408406
                _cons |   4.273791    .036038   118.59   0.000     4.203026    4.344555
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   4.273791    .036038   118.59   0.000     4.203026    4.344555
Expand welfare state  |   3.819444    .037704   101.30   0.000     3.745408     3.89348
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =     147.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0664
                                                Root MSE          =     1.2513

                              (Std. err. adjusted for 654 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .6672537   .0549767    12.14   0.000     .5593012    .7752061
                _cons |   3.607919   .0335814   107.44   0.000     3.541979     3.67386
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   3.607919   .0335814   107.44   0.000     3.541979     3.67386
Restrict immigration  |   4.275173   .0361521   118.26   0.000     4.204184    4.346161
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =     136.32
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0603
                                                Root MSE          =      1.262

                              (Std. err. adjusted for 653 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .6389451   .0547242    11.68   0.000     .5314881     .746402
                _cons |   3.721698   .0375495    99.11   0.000     3.647966    3.795431
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   3.721698   .0375495    99.11   0.000     3.647966    3.795431
Restrict immigration  |   4.360643   .0340169   128.19   0.000     4.293847    4.427439
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =      37.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0150
                                                Root MSE          =     1.2853

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Decrease CO2 tax  |   .3170203   .0520654     6.09   0.000     .2147845    .4192562
             _cons |   3.785385   .0328123   115.36   0.000     3.720954    3.849815
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Increase CO2 tax  |   3.785385   .0328123   115.36   0.000     3.720954    3.849815
 Decrease CO2 tax  |   4.102405   .0348803   117.61   0.000     4.033914    4.170896
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =       9.17
                                                Prob > F          =     0.0026
                                                R-squared         =     0.0038
                                                Root MSE          =     1.2994

                           (Std. err. adjusted for 653 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Decrease CO2 tax  |   .1602316   .0529263     3.03   0.003     .0563051     .264158
             _cons |   3.961974   .0364328   108.75   0.000     3.890434    4.033514
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Increase CO2 tax  |   3.961974   .0364328   108.75   0.000     3.890434    4.033514
 Decrease CO2 tax  |   4.122206   .0334847   123.11   0.000     4.056455    4.187956
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =      31.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0122
                                                Root MSE          =     1.2871

                               (Std. err. adjusted for 654 clusters in RespondentSerial)
----------------------------------------------------------------------------------------
                       |               Robust
           rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
More liberal abortion  |  -.2859182   .0507212    -5.64   0.000    -.3855145   -.1863218
                 _cons |   4.079941   .0307678   132.60   0.000     4.019525    4.140357
----------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
No change in abortion  |   4.079941   .0307678   132.60   0.000     4.019525    4.140357
More liberal abortion  |   3.794023   .0359039   105.67   0.000     3.723522    3.864524
----------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =       0.67
                                                Prob > F          =     0.4133
                                                R-squared         =     0.0003
                                                Root MSE          =     1.3016

                               (Std. err. adjusted for 653 clusters in RespondentSerial)
----------------------------------------------------------------------------------------
                       |               Robust
           rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
More liberal abortion  |  -.0433791   .0529949    -0.82   0.413    -.1474404    .0606822
                 _cons |   4.066872   .0354278   114.79   0.000     3.997305    4.136438
----------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
No change in abortion  |   4.066872   .0354278   114.79   0.000     3.997305    4.136438
More liberal abortion  |   4.023493    .034461   116.76   0.000     3.955825     4.09116
----------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =      30.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0122
                                                Root MSE          =     1.2871

                         (Std. err. adjusted for 654 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |  -.2913595   .0531285    -5.48   0.000    -.3956829   -.1870362
           _cons |   4.059923   .0296516   136.92   0.000     4.001699    4.118147
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   4.059923   .0296516   136.92   0.000     4.001699    4.118147
   Conflict appeal  |   3.768563   .0392629    95.98   0.000     3.691466     3.84566
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =      18.46
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0084
                                                Root MSE          =     1.2964

                         (Std. err. adjusted for 653 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |  -.2439389   .0567771    -4.30   0.000    -.3554269    -.132451
           _cons |   4.140764   .0317522   130.41   0.000     4.078415    4.203113
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   4.140764   .0317522   130.41   0.000     4.078415    4.203113
   Conflict appeal  |   3.896825   .0414207    94.08   0.000     3.815491     3.97816
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 653)         =       7.99
                                                Prob > F          =     0.0048
                                                R-squared         =     0.0033
                                                Root MSE          =     1.2929

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |   .1512363   .0534972     2.83   0.005     .0461891    .2562835
             _cons |   3.882353   .0311439   124.66   0.000     3.821199    3.943507
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   3.882353   .0311439   124.66   0.000     3.821199    3.943507
   Solidarity appeal  |   4.033589   .0377586   106.83   0.000     3.959446    4.107732
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(1, 652)         =       6.49
                                                Prob > F          =     0.0111
                                                R-squared         =     0.0029
                                                Root MSE          =     1.2999

                           (Std. err. adjusted for 653 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |   .1425375   .0559682     2.55   0.011     .0326378    .2524373
             _cons |   3.987606   .0330333   120.71   0.000     3.922741    4.052471
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   3.987606   .0330333   120.71   0.000     3.922741    4.052471
   Solidarity appeal  |   4.130144   .0392937   105.11   0.000     4.052986    4.207301
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(2, 653)         =      16.15
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0127
                                                Root MSE          =      1.287

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |  -.3451623   .0681033    -5.07   0.000    -.4788902   -.2114345
Solidarity appeal  |  -.0801362   .0684251    -1.17   0.242     -.214496    .0542235
                   |
             _cons |   4.113725   .0539584    76.24   0.000     4.007773    4.219678
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   4.113725   .0539584    76.24   0.000     4.007773    4.219678
  Conflict appeal  |   3.768563   .0392705    95.96   0.000     3.691452    3.845675
Solidarity appeal  |   4.033589   .0377659   106.80   0.000     3.959432    4.107747
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,578
                                                F(2, 652)         =       9.38
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0084
                                                Root MSE          =     1.2966

                           (Std. err. adjusted for 653 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |  -.2650794   .0734845    -3.61   0.000    -.4093742   -.1207845
Solidarity appeal  |  -.0317612   .0723143    -0.44   0.661    -.1737582    .1102358
                   |
             _cons |   4.161905   .0584962    71.15   0.000     4.047041    4.276768
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,578
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   4.161905   .0584962    71.15   0.000     4.047041    4.276768
  Conflict appeal  |   3.896825   .0414288    94.06   0.000     3.815475    3.978175
Solidarity appeal  |   4.130144   .0393013   105.09   0.000     4.052971    4.207316
------------------------------------------------------------------------------------

.  
. 
. coefplot  (age_candidate0    gender_candidate0 class_candidate0  rural_candidate0  policy_welfare0  policy_immigration0  policy_e
> nvironment0  policy_abort0  class_appeal20, label(Middle class) color(black))  (age_candidate1    gender_candidate1 class_candida
> te1  rural_candidate1  policy_welfare1  policy_immigration1  policy_environment1  policy_abort1 class_appeal21, label(Working cla
> ss)), drop(_cons)  legend(off)  xsc(r(3.4 4.4)) xlab(3.4 (.2) 4.4) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("
> Rightscale: Marginal means by class", size(medsmall))  

. graph save "mm_rightrating_class_no.gph", replace
file mm_rightrating_class_no.gph saved

. 
. 
. foreach x in $dimensions_no class_appeal2{
  2.         reg represented i.`x'  if workingclass==0,  cl(RespondentSerial)
  3.         margins i.`x', post
  4.         est store `x'0
  5.         reg represented i.`x'  if workingclass==1,  cl(RespondentSerial)
  6.         margins i.`x', post
  7.         est store `x'1
  8. } 

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       0.01
                                                Prob > F          =     0.9335
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3622

                      (Std. err. adjusted for 652 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
  represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |   .0047016   .0563276     0.08   0.934     -.105904    .1153072
        _cons |   3.529833   .0428276    82.42   0.000     3.445736     3.61393
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   3.529833   .0428276    82.42   0.000     3.445736     3.61393
          60  |   3.534535   .0447557    78.97   0.000     3.446651    3.622418
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       2.48
                                                Prob > F          =     0.1159
                                                R-squared         =     0.0010
                                                Root MSE          =     1.4725

                      (Std. err. adjusted for 656 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
  represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |  -.0918287   .0583342    -1.57   0.116    -.2063733    .0227159
        _cons |   3.661821   .0450605    81.26   0.000      3.57334    3.750301
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   3.661821   .0450605    81.26   0.000      3.57334    3.750301
          60  |   3.569992   .0483981    73.76   0.000     3.474958    3.665026
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       0.00
                                                Prob > F          =     0.9998
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3622

                         (Std. err. adjusted for 652 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |  -.0000125   .0537639    -0.00   1.000    -.1055842    .1055591
           _cons |   3.532258   .0440859    80.12   0.000      3.44569    3.618826
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   3.532258   .0440859    80.12   0.000      3.44569    3.618826
           Male  |   3.532246   .0419249    84.25   0.000     3.449921     3.61457
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       1.40
                                                Prob > F          =     0.2380
                                                R-squared         =     0.0005
                                                Root MSE          =     1.4729

                         (Std. err. adjusted for 656 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |  -.0676923   .0573098    -1.18   0.238    -.1802254    .0448408
           _cons |       3.65    .045796    79.70   0.000     3.560075    3.739925
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |       3.65    .045796    79.70   0.000     3.560075    3.739925
           Male  |   3.582308   .0470256    76.18   0.000     3.489969    3.674647
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       6.13
                                                Prob > F          =     0.0136
                                                R-squared         =     0.0023
                                                Root MSE          =     1.3606

                        (Std. err. adjusted for 652 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |  -.1317298   .0532121    -2.48   0.014    -.2362179   -.0272417
          _cons |   3.598905   .0430142    83.67   0.000     3.514442    3.683369
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   3.598905   .0430142    83.67   0.000     3.514442    3.683369
 Working class  |   3.467176   .0425572    81.47   0.000      3.38361    3.550742
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       0.01
                                                Prob > F          =     0.9278
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4733

                        (Std. err. adjusted for 656 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |   .0052758   .0581644     0.09   0.928    -.1089353    .1194869
          _cons |   3.613419    .046718    77.35   0.000     3.521683    3.705154
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   3.613419    .046718    77.35   0.000     3.521683    3.705154
 Working class  |   3.618694   .0466768    77.53   0.000      3.52704    3.710349
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       1.41
                                                Prob > F          =     0.2360
                                                R-squared         =     0.0006
                                                Root MSE          =     1.3618

                        (Std. err. adjusted for 652 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |   -.065954   .0556065    -1.19   0.236    -.1751437    .0432357
          _cons |   3.566694   .0435844    81.83   0.000     3.481111    3.652277
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   3.566694   .0435844    81.83   0.000     3.481111    3.652277
         Rural  |    3.50074   .0435839    80.32   0.000     3.415158    3.586322
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       0.36
                                                Prob > F          =     0.5479
                                                R-squared         =     0.0001
                                                Root MSE          =     1.4732

                        (Std. err. adjusted for 656 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |  -.0344171   .0572381    -0.60   0.548    -.1468094    .0779752
          _cons |   3.633971   .0449504    80.84   0.000     3.545707    3.722236
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   3.633971   .0449504    80.84   0.000     3.545707    3.722236
         Rural  |   3.599554   .0476947    75.47   0.000     3.505901    3.693207
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =      21.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0108
                                                Root MSE          =     1.3548

                              (Std. err. adjusted for 652 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |   .2836628   .0615565     4.61   0.000     .1627896     .404536
                _cons |   3.391571   .0458699    73.94   0.000       3.3015    3.481642
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   3.391571   .0458699    73.94   0.000       3.3015    3.481642
Expand welfare state  |   3.675234   .0450697    81.55   0.000     3.586734    3.763733
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       6.88
                                                Prob > F          =     0.0089
                                                R-squared         =     0.0033
                                                Root MSE          =     1.4708

                              (Std. err. adjusted for 656 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |   .1699383   .0647762     2.62   0.009     .0427442    .2971324
                _cons |   3.530596   .0488623    72.26   0.000     3.434651    3.626542
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   3.530596   .0488623    72.26   0.000     3.434651    3.626542
Expand welfare state  |   3.700535   .0490439    75.45   0.000     3.604232    3.796837
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       0.00
                                                Prob > F          =     0.9554
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3622

                              (Std. err. adjusted for 652 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .0034015   .0607885     0.06   0.955    -.1159637    .1227667
                _cons |   3.530549   .0462083    76.41   0.000     3.439814    3.621284
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   3.530549   .0462083    76.41   0.000     3.439814    3.621284
Restrict immigration  |   3.533951     .04438    79.63   0.000     3.446805    3.621096
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       7.83
                                                Prob > F          =     0.0053
                                                R-squared         =     0.0040
                                                Root MSE          =     1.4703

                              (Std. err. adjusted for 656 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .1860499   .0665027     2.80   0.005     .0554657    .3166341
                _cons |   3.521841   .0495485    71.08   0.000     3.424548    3.619134
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   3.521841   .0495485    71.08   0.000     3.424548    3.619134
Restrict immigration  |   3.707891   .0493172    75.18   0.000     3.611052     3.80473
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       1.73
                                                Prob > F          =     0.1885
                                                R-squared         =     0.0007
                                                Root MSE          =     1.3617

                           (Std. err. adjusted for 652 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Decrease CO2 tax  |    .074251   .0563998     1.32   0.188    -.0364964    .1849984
             _cons |   3.495399   .0444778    78.59   0.000     3.408062    3.582736
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Increase CO2 tax  |   3.495399   .0444778    78.59   0.000     3.408062    3.582736
 Decrease CO2 tax  |    3.56965   .0431623    82.70   0.000     3.484896    3.654404
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       6.24
                                                Prob > F          =     0.0127
                                                R-squared         =     0.0025
                                                Root MSE          =     1.4714

                           (Std. err. adjusted for 656 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Decrease CO2 tax  |   .1485481    .059475     2.50   0.013     .0317634    .2653328
             _cons |   3.539137   .0480269    73.69   0.000     3.444832    3.633443
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
 Increase CO2 tax  |   3.539137   .0480269    73.69   0.000     3.444832    3.633443
 Decrease CO2 tax  |   3.687685   .0462502    79.73   0.000     3.596869    3.778502
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       1.62
                                                Prob > F          =     0.2038
                                                R-squared         =     0.0007
                                                Root MSE          =     1.3617

                               (Std. err. adjusted for 652 clusters in RespondentSerial)
----------------------------------------------------------------------------------------
                       |               Robust
           represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
More liberal abortion  |  -.0736415   .0578959    -1.27   0.204    -.1873268    .0400438
                 _cons |   3.567608   .0424576    84.03   0.000     3.484237    3.650978
----------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
No change in abortion  |   3.567608   .0424576    84.03   0.000     3.484237    3.650978
More liberal abortion  |   3.493966   .0462124    75.61   0.000     3.403223     3.58471
----------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       0.27
                                                Prob > F          =     0.6042
                                                R-squared         =     0.0001
                                                Root MSE          =     1.4732

                               (Std. err. adjusted for 656 clusters in RespondentSerial)
----------------------------------------------------------------------------------------
                       |               Robust
           represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
More liberal abortion  |  -.0327747   .0631894    -0.52   0.604     -.156853    .0913036
                 _cons |   3.632466   .0468901    77.47   0.000     3.540393    3.724539
----------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
          policy_abort |
No change in abortion  |   3.632466   .0468901    77.47   0.000     3.540393    3.724539
More liberal abortion  |   3.599691   .0496992    72.43   0.000     3.502102     3.69728
----------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       4.29
                                                Prob > F          =     0.0387
                                                R-squared         =     0.0018
                                                Root MSE          =      1.361

                         (Std. err. adjusted for 652 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |  -.1174317      .0567    -2.07   0.039    -.2287686   -.0060947
           _cons |    3.57956    .039915    89.68   0.000     3.501183    3.657938
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |    3.57956    .039915    89.68   0.000     3.501183    3.657938
   Conflict appeal  |   3.462128   .0485602    71.30   0.000     3.366775    3.557482
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       2.12
                                                Prob > F          =     0.1458
                                                R-squared         =     0.0009
                                                Root MSE          =     1.4726

                         (Std. err. adjusted for 656 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |   .0928022   .0637339     1.46   0.146    -.0323452    .2179496
           _cons |   3.579747   .0429442    83.36   0.000     3.495422    3.664072
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   3.579747   .0429442    83.36   0.000     3.495422    3.664072
   Conflict appeal  |   3.672549   .0549617    66.82   0.000     3.564627    3.780471
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(1, 651)         =       0.73
                                                Prob > F          =     0.3935
                                                R-squared         =     0.0003
                                                Root MSE          =      1.362

                           (Std. err. adjusted for 652 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |   .0470762   .0551321     0.85   0.393    -.0611821    .1553345
             _cons |   3.513269   .0416808    84.29   0.000     3.431424    3.595114
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   3.513269   .0416808    84.29   0.000     3.431424    3.595114
   Solidarity appeal  |   3.560345   .0451774    78.81   0.000     3.471634    3.649056
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(1, 655)         =       3.56
                                                Prob > F          =     0.0597
                                                R-squared         =     0.0015
                                                Root MSE          =     1.4722

                           (Std. err. adjusted for 656 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |  -.1149616   .0609372    -1.89   0.060    -.2346175    .0046943
             _cons |   3.662581   .0448834    81.60   0.000     3.574448    3.750713
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   3.662581   .0448834    81.60   0.000     3.574448    3.750713
   Solidarity appeal  |   3.547619   .0504234    70.36   0.000     3.448608     3.64663
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,589
                                                F(2, 651)         =       2.43
                                                Prob > F          =     0.0886
                                                R-squared         =     0.0020
                                                Root MSE          =     1.3611

                           (Std. err. adjusted for 652 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |  -.1573934   .0763037    -2.06   0.040    -.3072246   -.0075623
Solidarity appeal  |  -.0591771    .074022    -0.80   0.424    -.2045278    .0861736
                   |
             _cons |   3.619522   .0660655    54.79   0.000     3.489795    3.749249
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,589
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   3.619522   .0660655    54.79   0.000     3.489795    3.749249
  Conflict appeal  |   3.462128   .0485696    71.28   0.000     3.366756      3.5575
Solidarity appeal  |   3.560345   .0451861    78.79   0.000     3.471617    3.649073
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      2,600
                                                F(2, 655)         =       1.80
                                                Prob > F          =     0.1663
                                                R-squared         =     0.0015
                                                Root MSE          =     1.4724

                           (Std. err. adjusted for 656 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |   .0291528   .0814448     0.36   0.720    -.1307716    .1890772
Solidarity appeal  |  -.0957772    .077642    -1.23   0.218    -.2482345    .0566801
                   |
             _cons |   3.643396   .0666807    54.64   0.000     3.512463     3.77433
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 2,600
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   3.643396   .0666807    54.64   0.000     3.512463     3.77433
  Conflict appeal  |   3.672549   .0549723    66.81   0.000     3.564606    3.780492
Solidarity appeal  |   3.547619   .0504331    70.34   0.000     3.448589    3.646649
------------------------------------------------------------------------------------

.  
. 
. coefplot  (age_candidate0    gender_candidate0 class_candidate0  rural_candidate0  policy_welfare0  policy_immigration0  policy_e
> nvironment0  policy_abort0  class_appeal20, label(Middle class) color(black))  (age_candidate1    gender_candidate1 class_candida
> te1  rural_candidate1  policy_welfare1  policy_immigration1  policy_environment1  policy_abort1 class_appeal21, label(Working cla
> ss)), drop(_cons)  legend(off)  xsc(r(3.3 4.0)) xlab(3.2 (.2) 4.0) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("
> Represented: Marginal means by class", size(medsmall))  

. graph save "mm_represented_class_no.gph", replace
file mm_represented_class_no.gph saved

. 
. clear 

. use "Data_Britain.dta"

. 
. foreach x in $dimensions_uk class_appeal2{
  2.         reg rightrating i.`x'  if workingclass==0,  cl(RespondentSerial)
  3.         margins i.`x', post
  4.         est store `x'0
  5.         reg rightrating i.`x'  if workingclass==1,  cl(RespondentSerial)
  6.         margins i.`x', post
  7.         est store `x'1
  8. } 

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.94
                                                Prob > F          =     0.3336
                                                R-squared         =     0.0003
                                                Root MSE          =     1.3872

                      (Std. err. adjusted for 962 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
  rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |   .0439551   .0454403     0.97   0.334    -.0452186    .1331287
        _cons |   4.044422   .0336285   120.27   0.000     3.978428    4.110416
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   4.044422   .0336285   120.27   0.000     3.978428    4.110416
          60  |   4.088377   .0309526   132.09   0.000     4.027635    4.149119
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       0.78
                                                Prob > F          =     0.3765
                                                R-squared         =     0.0002
                                                Root MSE          =      1.417

                    (Std. err. adjusted for 1,039 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
  rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |  -.0368747   .0416824    -0.88   0.377    -.1186661    .0449166
        _cons |   4.080928   .0282352   144.53   0.000     4.025523    4.136332
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   4.080928   .0282352   144.53   0.000     4.025523    4.136332
          60  |   4.044053    .028958   139.65   0.000      3.98723    4.100876
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.18
                                                Prob > F          =     0.6732
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3873

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |   .0180546   .0427902     0.42   0.673    -.0659184    .1020276
           _cons |   4.056712   .0315192   128.71   0.000     3.994857    4.118566
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   4.056712   .0315192   128.71   0.000     3.994857    4.118566
           Male  |   4.074766   .0313893   129.81   0.000     4.013167    4.136366
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       3.90
                                                Prob > F          =     0.0486
                                                R-squared         =     0.0009
                                                Root MSE          =     1.4165

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |   .0872232   .0441692     1.97   0.049     .0005521    .1738943
           _cons |   4.018895   .0304762   131.87   0.000     3.959093    4.078697
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   4.018895   .0304762   131.87   0.000     3.959093    4.078697
           Male  |   4.106119   .0285194   143.98   0.000     4.050156    4.162081
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      18.75
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0053
                                                Root MSE          =     1.3837

                        (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |   -.201007   .0464195    -4.33   0.000    -.2921022   -.1099118
          _cons |   4.166147   .0332356   125.35   0.000     4.100925     4.23137
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   4.166147   .0332356   125.35   0.000     4.100925     4.23137
 Working class  |    3.96514   .0321695   123.26   0.000      3.90201    4.028271
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      17.20
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0045
                                                Root MSE          =      1.414

                      (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |   -.189616   .0457196    -4.15   0.000    -.2793294   -.0999026
          _cons |   4.154963   .0299907   138.54   0.000     4.096113    4.213812
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   4.154963   .0299907   138.54   0.000     4.096113    4.213812
 Working class  |   3.965347   .0301895   131.35   0.000     3.906107    4.024586
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.09
                                                Prob > F          =     0.7683
                                                R-squared         =     0.0000
                                                Root MSE          =     1.3873

                        (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |   .0133842   .0454193     0.29   0.768    -.0757483    .1025167
          _cons |   4.058917   .0329046   123.35   0.000     3.994344     4.12349
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   4.058917   .0329046   123.35   0.000     3.994344     4.12349
         Rural  |   4.072301   .0318298   127.94   0.000     4.009838    4.134765
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       5.48
                                                Prob > F          =     0.0195
                                                R-squared         =     0.0013
                                                Root MSE          =     1.4162

                      (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |   .1019635    .043567     2.34   0.019      .016474     .187453
          _cons |   4.010396   .0296775   135.13   0.000     3.952161    4.068631
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   4.010396   .0296775   135.13   0.000     3.952161    4.068631
         Rural  |    4.11236   .0289266   142.17   0.000     4.055598    4.169121
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      84.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0253
                                                Root MSE          =     1.3697

                              (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |  -.4412682    .047868    -9.22   0.000     -.535206   -.3473304
                _cons |   4.286383   .0337934   126.84   0.000     4.220065      4.3527
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   4.286383   .0337934   126.84   0.000     4.220065      4.3527
Expand welfare state  |   3.845114    .032734   117.47   0.000     3.780876    3.909353
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      40.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0118
                                                Root MSE          =     1.4088

                            (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |  -.3078455   .0483486    -6.37   0.000    -.4027176   -.2129734
                _cons |   4.218279   .0314162   134.27   0.000     4.156633    4.279926
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   4.218279   .0314162   134.27   0.000     4.156633    4.279926
Expand welfare state  |   3.910434   .0309516   126.34   0.000     3.849699    3.971168
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =     118.53
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0358
                                                Root MSE          =     1.3623

                              (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .5249963   .0482222    10.89   0.000     .4303633    .6196293
                _cons |   3.802295   .0331438   114.72   0.000     3.737253    3.867338
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   3.802295   .0331438   114.72   0.000     3.737253    3.867338
Restrict immigration  |   4.327292   .0333673   129.69   0.000      4.26181    4.392773
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =     106.12
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0291
                                                Root MSE          =     1.3964

                            (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .4837594   .0469606    10.30   0.000     .3916109     .575908
                _cons |   3.821154    .031227   122.37   0.000     3.759879    3.882429
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   3.821154    .031227   122.37   0.000     3.759879    3.882429
Restrict immigration  |   4.304913   .0298579   144.18   0.000     4.246325    4.363502
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      48.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0132
                                                Root MSE          =     1.3781

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
Prioritize growth  |   .3190408   .0458474     6.96   0.000     .2290683    .4090133
             _cons |   3.907969   .0314732   124.17   0.000     3.846205    3.969733
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
 policy_environment |
Prioritize climate  |   3.907969   .0314732   124.17   0.000     3.846205    3.969733
 Prioritize growth  |    4.22701   .0335406   126.03   0.000     4.161189    4.292831
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       9.93
                                                Prob > F          =     0.0017
                                                R-squared         =     0.0026
                                                Root MSE          =     1.4153

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
Prioritize growth  |   .1438549   .0456459     3.15   0.002     .0542862    .2334236
             _cons |   3.989731   .0301898   132.15   0.000     3.930491    4.048971
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
 policy_environment |
Prioritize climate  |   3.989731   .0301898   132.15   0.000     3.930491    4.048971
 Prioritize growth  |   4.133586   .0299816   137.87   0.000     4.074754    4.192417
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      28.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0078
                                                Root MSE          =     1.3819

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
EU independence  |    .244259   .0453848     5.38   0.000     .1551942    .3333238
           _cons |   3.945015   .0323349   122.00   0.000      3.88156    4.008471
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
 EU cooperation  |   3.945015   .0323349   122.00   0.000      3.88156    4.008471
EU independence  |   4.189274   .0323666   129.43   0.000     4.125757    4.252792
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      18.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0047
                                                Root MSE          =     1.4138

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
EU independence  |   .1935156   .0454038     4.26   0.000     .1044218    .2826093
           _cons |   3.965717    .029937   132.47   0.000     3.906973    4.024461
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
 EU cooperation  |   3.965717    .029937   132.47   0.000     3.906973    4.024461
EU independence  |   4.159233   .0300507   138.41   0.000     4.100266      4.2182
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      23.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0069
                                                Root MSE          =     1.3825

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |  -.2351031   .0489239    -4.81   0.000    -.3311131   -.1390931
           _cons |   4.159533    .029399   141.49   0.000     4.101839    4.217227
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   4.159533    .029399   141.49   0.000     4.101839    4.217227
   Conflict appeal  |    3.92443   .0382021   102.73   0.000     3.849461    3.999399
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       9.00
                                                Prob > F          =     0.0028
                                                R-squared         =     0.0024
                                                Root MSE          =     1.4155

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |  -.1412712   .0470779    -3.00   0.003      -.23365   -.0488925
           _cons |    4.11882   .0271932   151.47   0.000      4.06546     4.17218
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |    4.11882   .0271932   151.47   0.000      4.06546     4.17218
   Conflict appeal  |   3.977549   .0343224   115.89   0.000     3.910199    4.044898
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       4.73
                                                Prob > F          =     0.0299
                                                R-squared         =     0.0013
                                                Root MSE          =     1.3865

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |   .1007516   .0463426     2.17   0.030     .0098072     .191696
             _cons |   4.025217   .0297917   135.11   0.000     3.966753    4.083682
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   4.025217   .0297917   135.11   0.000     3.966753    4.083682
   Solidarity appeal  |   4.125969   .0357989   115.25   0.000     4.055716    4.196222
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       0.02
                                                Prob > F          =     0.8915
                                                R-squared         =     0.0000
                                                Root MSE          =     1.4172

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |  -.0064534   .0473096    -0.14   0.892    -.0992869      .08638
             _cons |   4.065383   .0268683   151.31   0.000     4.012661    4.118105
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   4.065383   .0268683   151.31   0.000     4.012661    4.118105
   Solidarity appeal  |    4.05893   .0349063   116.28   0.000     3.990435    4.127425
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(2, 961)         =      12.64
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0076
                                                Root MSE          =     1.3822

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |   -.303021   .0649465    -4.67   0.000    -.4304743   -.1755678
Solidarity appeal  |   -.101482   .0613097    -1.66   0.098    -.2217983    .0188343
                   |
             _cons |   4.227451   .0504261    83.83   0.000     4.128493    4.326409
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   4.227451   .0504261    83.83   0.000     4.128493    4.326409
  Conflict appeal  |    3.92443   .0382071   102.71   0.000     3.849451    3.999409
Solidarity appeal  |   4.125969   .0358036   115.24   0.000     4.055707    4.196231
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(2, 1038)        =       8.53
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0045
                                                Root MSE          =     1.4141

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       rightrating | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |  -.2591378   .0627552    -4.13   0.000    -.3822794   -.1359963
Solidarity appeal  |  -.1777567   .0629343    -2.82   0.005    -.3012498   -.0542637
                   |
             _cons |   4.236686   .0491993    86.11   0.000     4.140145    4.333228
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   4.236686   .0491993    86.11   0.000     4.140145    4.333228
  Conflict appeal  |   3.977549   .0343265   115.87   0.000     3.910191    4.044906
Solidarity appeal  |    4.05893   .0349105   116.27   0.000     3.990426    4.127433
------------------------------------------------------------------------------------

.  
. 
. coefplot  (age_candidate0    gender_candidate0 class_candidate0  rural_candidate0  policy_welfare0  policy_immigration0  policy_e
> nvironment0  policy_eu0    class_appeal20, label(Middle class) color(black))  (age_candidate1    gender_candidate1 class_candidat
> e1  rural_candidate1  policy_welfare1  policy_immigration1  policy_environment1  policy_eu1   class_appeal21, label(Working class
> )), drop(_cons)  legend(off)  xsc(r(3.4 4.4)) xlab(3.4 (.2) 4.4)  plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("R
> ightscale: Marginal means by class", size(medsmall))  

. graph save "mm_rightrating_class_uk.gph", replace
file mm_rightrating_class_uk.gph saved

. 
. 
. foreach x in $dimensions_uk class_appeal2{
  2.         reg represented i.`x'  if workingclass==0,  cl(RespondentSerial)
  3.         margins i.`x', post
  4.         est store `x'0
  5.         reg represented i.`x'  if workingclass==1,  cl(RespondentSerial)
  6.         margins i.`x', post
  7.         est store `x'1
  8. } 

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.06
                                                Prob > F          =     0.8041
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5075

                      (Std. err. adjusted for 962 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
  represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |   .0122876   .0495123     0.25   0.804     -.084877    .1094522
        _cons |   3.604745   .0410856    87.74   0.000     3.524117    3.685373
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   3.604745   .0410856    87.74   0.000     3.524117    3.685373
          60  |   3.617033   .0418987    86.33   0.000     3.534809    3.699256
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       0.08
                                                Prob > F          =     0.7809
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5414

                    (Std. err. adjusted for 1,039 clusters in RespondentSerial)
-------------------------------------------------------------------------------
              |               Robust
  represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          60  |  -.0135436   .0486746    -0.28   0.781    -.1090555    .0819682
        _cons |   3.776148   .0388714    97.14   0.000     3.699872    3.852423
-------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
age_candidate |
          30  |   3.776148   .0388714    97.14   0.000     3.699872    3.852423
          60  |   3.762604   .0388703    96.80   0.000     3.686331    3.838877
-------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.51
                                                Prob > F          =     0.4736
                                                R-squared         =     0.0001
                                                Root MSE          =     1.5074

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |   .0361524   .0504332     0.72   0.474    -.0628196    .1351244
           _cons |   3.592612   .0422033    85.13   0.000     3.509791    3.675433
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   3.592612   .0422033    85.13   0.000     3.509791    3.675433
           Male  |   3.628764   .0413204    87.82   0.000     3.547676    3.709853
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       3.47
                                                Prob > F          =     0.0627
                                                R-squared         =     0.0008
                                                Root MSE          =     1.5407

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
           Male  |  -.0892946   .0479268    -1.86   0.063    -.1833391    .0047499
           _cons |   3.814438   .0379882   100.41   0.000     3.739896     3.88898
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
gender_candidate |
         Female  |   3.814438   .0379882   100.41   0.000     3.739896     3.88898
           Male  |   3.725143   .0392866    94.82   0.000     3.648053    3.802234
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       5.52
                                                Prob > F          =     0.0190
                                                R-squared         =     0.0014
                                                Root MSE          =     1.5065

                        (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |  -.1130756   .0481465    -2.35   0.019      -.20756   -.0185911
          _cons |   3.667186   .0412943    88.81   0.000     3.586148    3.748223
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   3.667186   .0412943    88.81   0.000     3.586148    3.748223
 Working class  |    3.55411   .0408879    86.92   0.000      3.47387     3.63435
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      16.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0042
                                                Root MSE          =     1.5381

                      (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
 Working class  |   .1999926   .0486153     4.11   0.000     .1045972     .295388
          _cons |   3.672285   .0386119    95.11   0.000     3.596518    3.748051
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_candidate |
  Middle class  |   3.672285   .0386119    95.11   0.000     3.596518    3.748051
 Working class  |   3.872277   .0390459    99.17   0.000     3.795659    3.948895
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.55
                                                Prob > F          =     0.4597
                                                R-squared         =     0.0001
                                                Root MSE          =     1.5074

                        (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |   .0359529   .0486088     0.74   0.460    -.0594387    .1313445
          _cons |   3.592357   .0430349    83.48   0.000     3.507903     3.67681
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   3.592357   .0430349    83.48   0.000     3.507903     3.67681
         Rural  |    3.62831    .039389    92.11   0.000     3.551011    3.705608
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       0.99
                                                Prob > F          =     0.3204
                                                R-squared         =     0.0002
                                                Root MSE          =     1.5412

                      (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------
                |               Robust
    represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Rural  |   .0485167   .0488068     0.99   0.320    -.0472546     .144288
          _cons |   3.744554   .0390815    95.81   0.000     3.667867    3.821242
---------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------
                |            Delta-method
                |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rural_candidate |
         Urban  |   3.744554   .0390815    95.81   0.000     3.667867    3.821242
         Rural  |   3.793071   .0387225    97.96   0.000     3.717088    3.869054
---------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      10.11
                                                Prob > F          =     0.0015
                                                R-squared         =     0.0032
                                                Root MSE          =     1.5051

                              (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |   .1704782    .053623     3.18   0.002     .0652465    .2757099
                _cons |   3.525468   .0426435    82.67   0.000     3.441783    3.609153
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   3.525468   .0426435    82.67   0.000     3.441783    3.609153
Expand welfare state  |   3.695946   .0427666    86.42   0.000     3.612019    3.779873
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       4.69
                                                Prob > F          =     0.0306
                                                R-squared         =     0.0014
                                                Root MSE          =     1.5403

                            (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
Expand welfare state  |   .1143916   .0528304     2.17   0.031      .010725    .2180582
                _cons |   3.711716   .0411118    90.28   0.000     3.631044    3.792388
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       policy_welfare |
        Reduce taxes  |   3.711716   .0411118    90.28   0.000     3.631044    3.792388
Expand welfare state  |   3.826108   .0393095    97.33   0.000     3.748973    3.903243
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.43
                                                Prob > F          =     0.5130
                                                R-squared         =     0.0001
                                                Root MSE          =     1.5074

                              (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .0360972   .0551622     0.65   0.513     -.072155    .1443494
                _cons |   3.592593    .041424    86.73   0.000     3.511301    3.673884
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   3.592593    .041424    86.73   0.000     3.511301    3.673884
Restrict immigration  |    3.62869   .0449662    80.70   0.000     3.540447    3.716933
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      22.93
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0068
                                                Root MSE          =     1.5361

                            (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
          represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
Restrict immigration  |   .2546095   .0531745     4.79   0.000     .1502677    .3589512
                _cons |   3.642308   .0392246    92.86   0.000     3.565339    3.719276
---------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
   policy_immigration |
 Liberal immigration  |   3.642308   .0392246    92.86   0.000     3.565339    3.719276
Restrict immigration  |   3.896917   .0414222    94.08   0.000     3.815636    3.978198
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =      26.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0077
                                                Root MSE          =     1.5017

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
Prioritize growth  |  -.2642468   .0512452    -5.16   0.000    -.3648121   -.1636815
             _cons |   3.741388   .0408198    91.66   0.000     3.661282    3.821494
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
 policy_environment |
Prioritize climate  |   3.741388   .0408198    91.66   0.000     3.661282    3.821494
 Prioritize growth  |   3.477141   .0430672    80.74   0.000     3.392625    3.561658
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =      19.52
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0051
                                                Root MSE          =     1.5374

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
policy_environment |
Prioritize growth  |  -.2198756   .0497692    -4.42   0.000    -.3175353    -.122216
             _cons |   3.881174    .038501   100.81   0.000     3.805625    3.956722
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
 policy_environment |
Prioritize climate  |   3.881174    .038501   100.81   0.000     3.805625    3.956722
 Prioritize growth  |   3.661298    .039778    92.04   0.000     3.583243    3.739353
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       7.57
                                                Prob > F          =     0.0061
                                                R-squared         =     0.0022
                                                Root MSE          =     1.5059

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
EU independence  |  -.1398982   .0508613    -2.75   0.006    -.2397102   -.0400862
           _cons |   3.679856   .0412101    89.29   0.000     3.598984    3.760728
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
 EU cooperation  |   3.679856   .0412101    89.29   0.000     3.598984    3.760728
EU independence  |   3.539958   .0425454    83.20   0.000     3.456465    3.623451
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       0.01
                                                Prob > F          =     0.9207
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5414

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
EU independence  |  -.0051843   .0520726    -0.10   0.921    -.1073638    .0969952
           _cons |   3.772091   .0390929    96.49   0.000     3.695381    3.848801
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       policy_eu |
 EU cooperation  |   3.772091   .0390929    96.49   0.000     3.695381    3.848801
EU independence  |   3.766906   .0407954    92.34   0.000     3.686856    3.846957
----------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.01
                                                Prob > F          =     0.9114
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5075

                         (Std. err. adjusted for 962 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |  -.0058905   .0529381    -0.11   0.911    -.1097781    .0979971
           _cons |   3.613057   .0400306    90.26   0.000     3.534499    3.691614
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   3.613057   .0400306    90.26   0.000     3.534499    3.691614
   Conflict appeal  |   3.607166   .0452594    79.70   0.000     3.518347    3.695985
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       0.80
                                                Prob > F          =     0.3706
                                                R-squared         =     0.0002
                                                Root MSE          =     1.5412

                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
----------------------------------------------------------------------------------
                 |               Robust
     represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 conflict_appeal |
Conflict appeal  |   .0451284   .0503799     0.90   0.371    -.0537297    .1439865
           _cons |   3.751595   .0363934   103.08   0.000     3.680182    3.823008
----------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

-------------------------------------------------------------------------------------
                    |            Delta-method
                    |     Margin   std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
    conflict_appeal |
No conflict appeal  |   3.751595   .0363934   103.08   0.000     3.680182    3.823008
   Conflict appeal  |   3.796723    .042819    88.67   0.000     3.712702    3.880745
-------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(1, 961)         =       0.35
                                                Prob > F          =     0.5519
                                                R-squared         =     0.0001
                                                Root MSE          =     1.5075

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |  -.0306662   .0515278    -0.60   0.552    -.1317862    .0704537
             _cons |   3.623043   .0387262    93.56   0.000     3.547046    3.699041
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   3.623043   .0387262    93.56   0.000     3.547046    3.699041
   Solidarity appeal  |   3.592377   .0459792    78.13   0.000     3.502146    3.682608
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(1, 1038)        =       0.05
                                                Prob > F          =     0.8232
                                                R-squared         =     0.0000
                                                Root MSE          =     1.5414

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
 solidarity_appeal |
Solidarity appeal  |  -.0116902   .0523059    -0.22   0.823    -.1143275    .0909471
             _cons |   3.774168   .0373656   101.01   0.000     3.700847    3.847488
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |     Margin   std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
    solidarity_appeal |
No solidarity appeal  |   3.774168   .0373656   101.01   0.000     3.700847    3.847488
   Solidarity appeal  |   3.762477   .0429463    87.61   0.000     3.678206    3.846749
---------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      3,848
                                                F(2, 961)         =       0.43
                                                Prob > F          =     0.6528
                                                R-squared         =     0.0002
                                                Root MSE          =     1.5076

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |  -.0477358   .0700896    -0.68   0.496    -.1852823    .0898106
Solidarity appeal  |  -.0625247   .0681349    -0.92   0.359     -.196235    .0711856
                   |
             _cons |   3.654902   .0606722    60.24   0.000     3.535837    3.773967
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 3,848
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   3.654902   .0606722    60.24   0.000     3.535837    3.773967
  Conflict appeal  |   3.607166   .0452653    79.69   0.000     3.518336    3.695996
Solidarity appeal  |   3.592377   .0459852    78.12   0.000     3.502134     3.68262
------------------------------------------------------------------------------------

Linear regression                               Number of obs     =      4,156
                                                F(2, 1038)        =       0.51
                                                Prob > F          =     0.5987
                                                R-squared         =     0.0003
                                                Root MSE          =     1.5414

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
       represented | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
  Conflict appeal  |   .0665458   .0681867     0.98   0.329    -.0672536    .2003452
Solidarity appeal  |   .0322999   .0706916     0.46   0.648    -.1064148    .1710147
                   |
             _cons |   3.730178   .0601095    62.06   0.000     3.612227    3.848128
------------------------------------------------------------------------------------

Adjusted predictions                                     Number of obs = 4,156
Model VCE: Robust

Expression: Linear prediction, predict()

------------------------------------------------------------------------------------
                   |            Delta-method
                   |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     class_appeal2 |
   Turnout appeal  |   3.730178   .0601095    62.06   0.000     3.612227    3.848128
  Conflict appeal  |   3.796723   .0428241    88.66   0.000     3.712692    3.880755
Solidarity appeal  |   3.762477   .0429515    87.60   0.000     3.678196    3.846759
------------------------------------------------------------------------------------

.  
. 
. coefplot  (age_candidate0    gender_candidate0 class_candidate0  rural_candidate0  policy_welfare0  policy_immigration0  policy_e
> nvironment0  policy_eu0    class_appeal20, label(Middle class) color(black))  (age_candidate1    gender_candidate1 class_candidat
> e1  rural_candidate1  policy_welfare1  policy_immigration1  policy_environment1  policy_eu1   class_appeal21, label(Working class
> )), drop(_cons)  legend(off)  xsc(r(3.3 4.0)) xlab(3.2 (.2) 4.0)  plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("R
> epresented: Marginal means by class", size(medsmall))  

. graph save "mm_represented_class_uk.gph", replace
file mm_represented_class_uk.gph saved

. 
. gr combine "mm_rightrating_class_no.gph"  "mm_represented_class_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "mm_rightrating_represented_no.gph", replace
file mm_rightrating_represented_no.gph saved

. gr combine  "mm_rightrating_class_uk.gph"  "mm_represented_class_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "mm_rightrating_represented_uk.gph", replace
file mm_rightrating_represented_uk.gph saved

. grc1leg2 "mm_rightrating_represented_no.gph" "mm_rightrating_represented_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) co
> l(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. 
. gr save "figureA5.gph", replace
file figureA5.gph saved

. gr export  "figureA5.pdf",as(pdf) replace
file figureA5.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *FIGURE A6**********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. global dimensions2 age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration policy_enviro
> nment policy_abort class_appeal

. global dimensions_interactions2 age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration 
> policy_environment policy_abort  class_appeal age_candidateXwc gender_candidateXwc class_candidateXwc rural_candidateXwc policy_w
> elfareXwc policy_immigrationXwc policy_environmentXwc policy_abortXwc class_appealXwc  

. 
. reg selected $dimensions2 if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,556
                                                F(9, 647)         =       2.52
                                                Prob > F          =     0.0077
                                                R-squared         =     0.0083
                                                Root MSE          =     .49889

                           (Std. err. adjusted for 648 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0114121   .0203447    -0.56   0.575    -.0513616    .0285375
  gender_candidate |  -.0520665   .0194499    -2.68   0.008    -.0902591    -.013874
   class_candidate |   -.013687   .0196919    -0.70   0.487    -.0523547    .0249808
   rural_candidate |   .0218293   .0198331     1.10   0.271    -.0171156    .0607743
    policy_welfare |    .029014   .0208341     1.39   0.164    -.0118965    .0699246
policy_immigration |   .0323136   .0198156     1.63   0.103    -.0065971    .0712243
policy_environment |  -.0302545   .0204836    -1.48   0.140    -.0704768    .0099678
      policy_abort |  -.0111895   .0200187    -0.56   0.576    -.0504989      .02812
      class_appeal |   -.056426   .0258822    -2.18   0.030    -.1072494   -.0056027
             _cons |   .5625949   .0340793    16.51   0.000     .4956754    .6295144
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions2 if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,564
                                                F(9, 653)         =       3.18
                                                Prob > F          =     0.0009
                                                R-squared         =     0.0110
                                                Root MSE          =     .49821

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0348122    .020014    -1.74   0.082    -.0741119    .0044874
  gender_candidate |   .0018189   .0196158     0.09   0.926    -.0366987    .0403365
   class_candidate |   .0219871   .0202362     1.09   0.278    -.0177488    .0617229
   rural_candidate |     .00938    .020577     0.46   0.649    -.0310252    .0497851
    policy_welfare |   .0575902   .0206088     2.79   0.005     .0171228    .0980577
policy_immigration |   .0487211   .0201632     2.42   0.016     .0091285    .0883137
policy_environment |    .052174   .0200484     2.60   0.009     .0128069     .091541
      policy_abort |  -.0079342   .0201323    -0.39   0.694     -.047466    .0315976
      class_appeal |   .0237421   .0254098     0.93   0.350    -.0261527    .0736369
             _cons |   .4040533   .0351827    11.48   0.000     .3349684    .4731381
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid) xsc(r(-.2 .2)) xlab(
> -.2 (.1) .2) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) titl
> e("AMCE by class", size(medsmall))

. graph save "amce_alt1_no.gph", replace
file amce_alt1_no.gph saved

.  
. reg selected $dimensions_interactions2 workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      5,120
                                                F(19, 1301)       =       2.70
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0097
                                                Root MSE          =     .49855

                            (Std. err. adjusted for 1,302 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |  -.0114121   .0203387    -0.56   0.575    -.0513123    .0284882
     gender_candidate |  -.0520665   .0194442    -2.68   0.008     -.090212   -.0139211
      class_candidate |   -.013687   .0196861    -0.70   0.487     -.052307    .0249331
      rural_candidate |   .0218293   .0198273     1.10   0.271    -.0170676    .0607263
       policy_welfare |    .029014   .0208279     1.39   0.164     -.011846    .0698741
   policy_immigration |   .0323136   .0198098     1.63   0.103    -.0065491    .0711763
   policy_environment |  -.0302545   .0204776    -1.48   0.140    -.0704271    .0099182
         policy_abort |  -.0111895   .0200128    -0.56   0.576    -.0504504    .0280715
         class_appeal |   -.056426   .0258747    -2.18   0.029    -.1071867   -.0056654
     age_candidateXwc |  -.0234002   .0285307    -0.82   0.412    -.0793714     .032571
  gender_candidateXwc |   .0538854   .0276159     1.95   0.051    -.0002912    .1080621
   class_candidateXwc |    .035674   .0282279     1.26   0.207    -.0197033    .0910513
   rural_candidateXwc |  -.0124494   .0285709    -0.44   0.663    -.0684995    .0436007
    policy_welfareXwc |   .0285762   .0292965     0.98   0.330    -.0288974    .0860498
policy_immigrationXwc |   .0164076   .0282623     0.58   0.562    -.0390371    .0718522
policy_environmentXwc |   .0824284   .0286539     2.88   0.004     .0262156    .1386412
      policy_abortXwc |   .0032553    .028383     0.11   0.909    -.0524262    .0589367
      class_appealXwc |   .0801681   .0362601     2.21   0.027     .0090335    .1513028
         workingclass |  -.1585416   .0489678    -3.24   0.001    -.2546062   -.0624771
                _cons |   .5625949   .0340694    16.51   0.000      .495758    .6294318
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions2 workingclass) ylabel(, nogrid) xsc(r(-.1 .2)) xlab(-.1 (.1) .2) xline(0, lpat(shortdas
> h_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by class", size(medsmall)) 

. graph save "amce_alt1_diff_no.gph", replace
file amce_alt1_diff_no.gph saved

. 
. clear 

. use "Data_Britain.dta"

. 
. global dimensions2 age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration policy_enviro
> nment policy_eu class_appeal

. 
. global dimensions_interactions2 age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration 
> policy_environment policy_eu  class_appeal age_candidateXwc gender_candidateXwc class_candidateXwc rural_candidateXwc policy_welf
> areXwc policy_immigrationXwc policy_environmentXwc policy_euXwc class_appealXwc  

. 
. reg selected $dimensions2 if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      3,848
                                                F(9, 961)         =       9.53
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0233
                                                Root MSE          =     .49478

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0098846   .0164451    -0.60   0.548     -.042157    .0223879
  gender_candidate |  -.0077932   .0161509    -0.48   0.630    -.0394883    .0239018
   class_candidate |  -.0224314   .0162686    -1.38   0.168    -.0543575    .0094948
   rural_candidate |  -.0006342   .0164485    -0.04   0.969    -.0329133    .0316448
    policy_welfare |   .0428373   .0168751     2.54   0.011      .009721    .0759537
policy_immigration |   .0493473   .0164919     2.99   0.003     .0169831    .0817115
policy_environment |  -.1094196   .0169798    -6.44   0.000    -.1427415   -.0760978
         policy_eu |   -.060313   .0167739    -3.60   0.000    -.0932306   -.0273953
      class_appeal |  -.0684026   .0206107    -3.32   0.001    -.1088498   -.0279553
             _cons |   .6127704   .0284946    21.50   0.000     .5568516    .6686891
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions2 if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      4,156
                                                F(9, 1038)        =      12.32
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0253
                                                Root MSE          =     .49423

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0167647   .0157895    -1.06   0.289    -.0477478    .0142183
  gender_candidate |  -.0494524   .0149661    -3.30   0.001    -.0788196   -.0200851
   class_candidate |   .0541808   .0155855     3.48   0.001     .0235981    .0847635
   rural_candidate |  -.0048752   .0153765    -0.32   0.751    -.0350478    .0252974
    policy_welfare |   .0500536   .0157262     3.18   0.002     .0191948    .0809124
policy_immigration |   .1117003   .0161212     6.93   0.000     .0800665    .1433341
policy_environment |  -.0629289   .0156579    -4.02   0.000    -.0936535   -.0322042
         policy_eu |  -.0179522   .0160995    -1.12   0.265    -.0495436    .0136391
      class_appeal |   .0179216   .0198844     0.90   0.368    -.0210965    .0569397
             _cons |   .4549219   .0270682    16.81   0.000     .4018073    .5080365
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid) xsc(r(-.2 .2)) xlab(
> -.2 (.1) .2) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) titl
> e("AMCE by class", size(medsmall))

. graph save "amce_alt1_uk.gph", replace
file amce_alt1_uk.gph saved

.  
. reg selected $dimensions_interactions2 workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(19, 2000)       =      10.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0243
                                                Root MSE          =     .49449

                            (Std. err. adjusted for 2,001 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |  -.0098846   .0164409    -0.60   0.548    -.0421277    .0223586
     gender_candidate |  -.0077932   .0161468    -0.48   0.629    -.0394595    .0238731
      class_candidate |  -.0224314   .0162645    -1.38   0.168    -.0543286    .0094658
      rural_candidate |  -.0006342   .0164443    -0.04   0.969     -.032884    .0316155
       policy_welfare |   .0428373   .0168709     2.54   0.011      .009751    .0759236
   policy_immigration |   .0493473   .0164877     2.99   0.003     .0170124    .0816821
   policy_environment |  -.1094196   .0169756    -6.45   0.000    -.1427113    -.076128
            policy_eu |   -.060313   .0167696    -3.60   0.000    -.0932008   -.0274252
         class_appeal |  -.0684026   .0206055    -3.32   0.001    -.1088131    -.027992
     age_candidateXwc |  -.0068801   .0227937    -0.30   0.763    -.0515819    .0378217
  gender_candidateXwc |  -.0416592   .0220147    -1.89   0.059    -.0848332    .0015149
   class_candidateXwc |   .0766122   .0225251     3.40   0.001     .0324371    .1207874
   rural_candidateXwc |   -.004241    .022512    -0.19   0.851    -.0483905    .0399085
    policy_welfareXwc |   .0072162   .0230625     0.31   0.754    -.0380127    .0524452
policy_immigrationXwc |    .062353    .023058     2.70   0.007     .0171329    .1075732
policy_environmentXwc |   .0464908   .0230928     2.01   0.044     .0012024    .0917792
         policy_euXwc |   .0423607   .0232454     1.82   0.069     -.003227    .0879485
      class_appealXwc |   .0863242   .0286334     3.01   0.003     .0301697    .1424787
         workingclass |  -.1578485   .0392941    -4.02   0.000    -.2349102   -.0807867
                _cons |   .6127704   .0284874    21.51   0.000     .5569023    .6686384
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions2 workingclass) ylabel(, nogrid) xsc(r(-.1 .2)) xlab(-.1 (.1) .2) xline(0, lpat(shortdas
> h_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by class", size(medsmall)) 

. graph save "amce_alt1_diff_uk.gph", replace
file amce_alt1_diff_uk.gph saved

. 
. gr combine "amce_alt1_no.gph" "amce_alt1_diff_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "binaryappeal_no.gph", replace
file binaryappeal_no.gph saved

. gr combine "amce_alt1_uk.gph" "amce_alt1_diff_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "binaryappeal_uk.gph", replace
file binaryappeal_uk.gph saved

. grc1leg2 "binaryappeal_no.gph" "binaryappeal_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA6.gph", replace
file figureA6.gph saved

. gr export  "figureA6.pdf",as(pdf) replace
file figureA6.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *FIGURE A7**********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. global dimensions_full age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration policy_en
> vironment policy_abort attention_appeal powerful_appeal interests_appeal common_appeal

.  
. global dimensions_int_full age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration polic
> y_environment policy_abort attention_appeal powerful_appeal interests_appeal common_appeal age_candidateXwc gender_candidateXwc c
> lass_candidateXwc rural_candidateXwc policy_welfareXwc policy_immigrationXwc policy_environmentXwc policy_abortXwc attention_appe
> alXwc powerful_appealXwc interests_appealXwc common_appealXwc

. 
. reg selected $dimensions_full if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,556
                                                F(12, 647)        =       2.05
                                                Prob > F          =     0.0183
                                                R-squared         =     0.0090
                                                Root MSE          =       .499

                           (Std. err. adjusted for 648 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0113008   .0202994    -0.56   0.578    -.0511615    .0285599
  gender_candidate |  -.0519633   .0194929    -2.67   0.008    -.0902404   -.0136862
   class_candidate |   -.014375   .0197213    -0.73   0.466    -.0531005    .0243504
   rural_candidate |   .0223608   .0197999     1.13   0.259    -.0165191    .0612406
    policy_welfare |   .0290848   .0208514     1.39   0.164    -.0118597    .0700293
policy_immigration |   .0323283   .0198463     1.63   0.104    -.0066427    .0712993
policy_environment |    -.02969   .0204967    -1.45   0.148    -.0699381     .010558
      policy_abort |  -.0104366   .0200033    -0.52   0.602    -.0497159    .0288426
  attention_appeal |  -.0701091   .0316246    -2.22   0.027    -.1322084   -.0080098
   powerful_appeal |   -.065768   .0319682    -2.06   0.040    -.1285419   -.0029941
  interests_appeal |   -.058713    .033283    -1.76   0.078    -.1240687    .0066426
     common_appeal |  -.0322576   .0317837    -1.01   0.311    -.0946693     .030154
             _cons |   .5619005   .0341074    16.47   0.000     .4949259     .628875
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_full if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,564
                                                F(12, 653)        =       2.87
                                                Prob > F          =     0.0007
                                                R-squared         =     0.0127
                                                Root MSE          =     .49807

                           (Std. err. adjusted for 654 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0354574   .0200149    -1.77   0.077    -.0747587    .0038439
  gender_candidate |   .0010669   .0196217     0.05   0.957    -.0374624    .0395961
   class_candidate |   .0223551   .0202601     1.10   0.270    -.0174278    .0621379
   rural_candidate |   .0079346   .0206283     0.38   0.701    -.0325713    .0484405
    policy_welfare |    .058571   .0206221     2.84   0.005     .0180773    .0990646
policy_immigration |   .0481094   .0201418     2.39   0.017     .0085588    .0876599
policy_environment |   .0511882   .0200495     2.55   0.011     .0118189    .0905575
      policy_abort |  -.0074304   .0201525    -0.37   0.712    -.0470019    .0321412
  attention_appeal |   .0267131   .0334568     0.80   0.425    -.0389828     .092409
   powerful_appeal |   .0607765   .0313894     1.94   0.053    -.0008599    .1224128
  interests_appeal |   .0069974   .0318658     0.22   0.826    -.0555744    .0695691
     common_appeal |   .0013967   .0312189     0.04   0.964     -.059905    .0626983
             _cons |   .4053716   .0352826    11.49   0.000     .3360907    .4746526
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid) xsc(r(-.2 .2)) xlab(
> -.2 (.1) .2) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) titl
> e("AMCE by class", size(medsmall))

. graph save "amce_alt2_no.gph", replace
file amce_alt2_no.gph saved

. 
. reg selected $dimensions_int_full workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      5,120
                                                F(25, 1301)       =       2.36
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0109
                                                Root MSE          =     .49854

                            (Std. err. adjusted for 1,302 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |  -.0113008   .0202935    -0.56   0.578    -.0511123    .0285107
     gender_candidate |  -.0519633   .0194872    -2.67   0.008    -.0901931   -.0137335
      class_candidate |   -.014375   .0197155    -0.73   0.466    -.0530527    .0243026
      rural_candidate |   .0223608   .0197941     1.13   0.259    -.0164711    .0611926
       policy_welfare |   .0290848   .0208452     1.40   0.163    -.0118092    .0699787
   policy_immigration |   .0323283   .0198405     1.63   0.103    -.0065946    .0712512
   policy_environment |    -.02969   .0204907    -1.45   0.148    -.0698884    .0105083
         policy_abort |  -.0104366   .0199974    -0.52   0.602    -.0496674    .0287941
     attention_appeal |  -.0701091   .0316153    -2.22   0.027    -.1321317   -.0080865
      powerful_appeal |   -.065768   .0319588    -2.06   0.040    -.1284644   -.0030716
     interests_appeal |   -.058713   .0332732    -1.76   0.078     -.123988    .0065619
        common_appeal |  -.0322576   .0317743    -1.02   0.310    -.0945922     .030077
     age_candidateXwc |  -.0241566    .028499    -0.85   0.397    -.0800657    .0317525
  gender_candidateXwc |   .0530302   .0276505     1.92   0.055    -.0012142    .1072745
   class_candidateXwc |   .0367301   .0282656     1.30   0.194    -.0187211    .0921812
   rural_candidateXwc |  -.0144262   .0285849    -0.50   0.614    -.0705038    .0416514
    policy_welfareXwc |   .0294862   .0293182     1.01   0.315      -.02803    .0870023
policy_immigrationXwc |   .0157811   .0282686     0.56   0.577    -.0396759     .071238
policy_environmentXwc |   .0808782    .028664     2.82   0.005     .0246455    .1371109
      policy_abortXwc |   .0030062   .0283865     0.11   0.916    -.0526821    .0586946
  attention_appealXwc |   .0968222   .0460246     2.10   0.036     .0065317    .1871128
   powerful_appealXwc |   .1265444   .0447896     2.83   0.005     .0386767    .2144122
  interests_appealXwc |   .0657104   .0460648     1.43   0.154     -.024659    .1560798
     common_appealXwc |   .0336543   .0445386     0.76   0.450    -.0537211    .1210296
         workingclass |  -.1565288   .0490591    -3.19   0.001    -.2527724   -.0602853
                _cons |   .5619005   .0340974    16.48   0.000     .4950086    .6287923
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_full workingclass) ylabel(, nogrid) xsc(r(-.1 .2)) xlab(-.1 (.1) .2) xline(0, lpat(shor
> tdash_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by class", size(medsmal
> l)) 

. graph save "amce_alt2_diff_no.gph", replace
file amce_alt2_diff_no.gph saved

. 
. clear 

. use "Data_Britain.dta"

. 
. global dimensions_full age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration policy_en
> vironment policy_eu attention_appeal powerful_appeal interests_appeal common_appeal

. 
. global dimensions_int_full age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration polic
> y_environment policy_eu attention_appeal powerful_appeal interests_appeal common_appeal age_candidateXwc gender_candidateXwc clas
> s_candidateXwc rural_candidateXwc policy_welfareXwc policy_immigrationXwc policy_environmentXwc policy_euXwc attention_appealXwc 
> powerful_appealXwc interests_appealXwc common_appealXwc

.  
. reg selected $dimensions_full if workingclass==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      3,848
                                                F(12, 961)        =       7.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0238
                                                Root MSE          =     .49486

                           (Std. err. adjusted for 962 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0100369   .0164661    -0.61   0.542    -.0423506    .0222768
  gender_candidate |  -.0078645   .0161627    -0.49   0.627    -.0395829    .0238538
   class_candidate |  -.0222586   .0162606    -1.37   0.171    -.0541691    .0096518
   rural_candidate |   -.000732   .0164571    -0.04   0.965    -.0330279    .0315639
    policy_welfare |   .0432542   .0168753     2.56   0.011     .0101374    .0763709
policy_immigration |   .0495648   .0164916     3.01   0.003     .0172011    .0819285
policy_environment |  -.1089615   .0170066    -6.41   0.000    -.1423359   -.0755871
         policy_eu |  -.0601717    .016795    -3.58   0.000    -.0931308   -.0272126
  attention_appeal |  -.0681715   .0258543    -2.64   0.009    -.1189089    -.017434
   powerful_appeal |  -.0608038   .0259261    -2.35   0.019    -.1116822   -.0099254
  interests_appeal |  -.0576839    .025465    -2.27   0.024    -.1076573   -.0077105
     common_appeal |  -.0873646   .0262952    -3.32   0.001    -.1389672    -.035762
             _cons |   .6122267   .0284906    21.49   0.000     .5563158    .6681377
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_full if workingclass==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      4,156
                                                F(12, 1038)       =      11.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0312
                                                Root MSE          =      .4929

                         (Std. err. adjusted for 1,039 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0167398   .0157297    -1.06   0.287    -.0476054    .0141258
  gender_candidate |  -.0508811   .0149581    -3.40   0.001    -.0802326   -.0215295
   class_candidate |   .0528799   .0155701     3.40   0.001     .0223274    .0834325
   rural_candidate |  -.0049079   .0153471    -0.32   0.749    -.0350226    .0252069
    policy_welfare |   .0490259   .0156946     3.12   0.002     .0182291    .0798227
policy_immigration |   .1115379   .0160758     6.94   0.000     .0799932    .1430826
policy_environment |  -.0636502   .0156147    -4.08   0.000    -.0942901   -.0330103
         policy_eu |  -.0178907    .016149    -1.11   0.268     -.049579    .0137976
  attention_appeal |   .0910742   .0248249     3.67   0.000     .0423614     .139787
   powerful_appeal |  -.0103361   .0247654    -0.42   0.676    -.0589321    .0382598
  interests_appeal |   .0053932   .0253829     0.21   0.832    -.0444144    .0552008
     common_appeal |  -.0154314     .02464    -0.63   0.531    -.0637814    .0329185
             _cons |   .4571829   .0271279    16.85   0.000     .4039511    .5104146
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid) xsc(r(-.2 .2)) xlab(
> -.2 (.1) .2) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) titl
> e("AMCE by class", size(medsmall))

. graph save "amce_alt2_uk.gph", replace
file amce_alt2_uk.gph saved

. 
. reg selected $dimensions_int_full workingclass, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(25, 2000)       =       9.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0276
                                                Root MSE          =     .49384

                            (Std. err. adjusted for 2,001 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |  -.0100369   .0164617    -0.61   0.542    -.0423208     .022247
     gender_candidate |  -.0078645   .0161584    -0.49   0.627    -.0395536    .0238245
      class_candidate |  -.0222586   .0162563    -1.37   0.171    -.0541397    .0096224
      rural_candidate |   -.000732   .0164526    -0.04   0.965    -.0329981    .0315341
       policy_welfare |   .0432542   .0168708     2.56   0.010      .010168    .0763404
   policy_immigration |   .0495648   .0164872     3.01   0.003     .0172309    .0818986
   policy_environment |  -.1089615   .0170021    -6.41   0.000    -.1423051   -.0756179
            policy_eu |  -.0601717   .0167905    -3.58   0.000    -.0931004    -.027243
     attention_appeal |  -.0681715   .0258474    -2.64   0.008    -.1188621   -.0174808
      powerful_appeal |  -.0608038   .0259192    -2.35   0.019    -.1116353   -.0099724
     interests_appeal |  -.0576839   .0254582    -2.27   0.024    -.1076112   -.0077566
        common_appeal |  -.0873646   .0262881    -3.32   0.001    -.1389196   -.0358096
     age_candidateXwc |  -.0067029   .0227674    -0.29   0.768    -.0513533    .0379474
  gender_candidateXwc |  -.0430165   .0220179    -1.95   0.051     -.086197    .0001639
   class_candidateXwc |   .0751386   .0225087     3.34   0.001     .0309956    .1192815
   rural_candidateXwc |  -.0041758   .0224982    -0.19   0.853    -.0482982    .0399465
    policy_welfareXwc |   .0057718    .023041     0.25   0.802    -.0394152    .0509587
policy_immigrationXwc |   .0619731    .023026     2.69   0.007     .0168156    .1071307
policy_environmentXwc |   .0453114   .0230832     1.96   0.050     .0000418    .0905809
         policy_euXwc |    .042281   .0232949     1.82   0.070    -.0034038    .0879657
  attention_appealXwc |   .1592456   .0358361     4.44   0.000     .0889656    .2295256
   powerful_appealXwc |   .0504677   .0358468     1.41   0.159    -.0198332    .1207686
  interests_appealXwc |   .0630772   .0359481     1.75   0.079    -.0074224    .1335767
     common_appealXwc |   .0719332   .0360286     2.00   0.046     .0012757    .1425907
         workingclass |  -.1550438   .0393323    -3.94   0.000    -.2321805   -.0779072
                _cons |   .6122267    .028483    21.49   0.000     .5563674    .6680861
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_full workingclass) ylabel(, nogrid) xsc(r(-.1 .2)) xlab(-.1 (.1) .2) xline(0, lpat(shor
> tdash_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by class", size(medsmal
> l)) 

. graph save "amce_alt2_diff_uk.gph", replace
file amce_alt2_diff_uk.gph saved

. 
. gr combine "amce_alt2_no.gph" "amce_alt2_diff_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "fullappeal_no.gph", replace
file fullappeal_no.gph saved

. gr combine "amce_alt2_uk.gph" "amce_alt2_diff_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "fullappeal_uk.gph", replace
file fullappeal_uk.gph saved

. grc1leg2 "fullappeal_no.gph" "fullappeal_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA7.gph", replace
file figureA7.gph saved

. gr export  "figureA7.pdf",as(pdf) replace
file figureA7.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *FIGURE A8**********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg male $dimensions_no, cl(RespondentSerial)

Linear regression                               Number of obs     =     12,760
                                                F(10, 3189)       =       0.48
                                                Prob > F          =     0.9039
                                                R-squared         =     0.0004
                                                Root MSE          =     .50012

                         (Std. err. adjusted for 3,190 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
              male | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0035573   .0088254    -0.40   0.687    -.0208612    .0137467
  gender_candidate |   .0100597   .0088536     1.14   0.256    -.0072996    .0274189
   class_candidate |   .0075714   .0088116     0.86   0.390    -.0097055    .0248483
   rural_candidate |  -.0083022   .0088195    -0.94   0.347    -.0255946    .0089902
    policy_welfare |  -.0016348   .0088695    -0.18   0.854    -.0190253    .0157556
policy_immigration |   .0003737   .0089784     0.04   0.967    -.0172303    .0179778
policy_environment |   .0111055   .0089081     1.25   0.213    -.0063607    .0285716
      policy_abort |   .0000739   .0088478     0.01   0.993     -.017274    .0174219
   conflict_appeal |  -.0022933   .0121988    -0.19   0.851    -.0262116     .021625
 solidarity_appeal |    .000346    .012183     0.03   0.977    -.0235414    .0242334
             _cons |   .4904204   .0178899    27.41   0.000     .4553436    .5254972
------------------------------------------------------------------------------------

. est store m1

. reg uniedu $dimensions_no, cl(RespondentSerial)

Linear regression                               Number of obs     =     12,760
                                                F(10, 3189)       =       1.30
                                                Prob > F          =     0.2251
                                                R-squared         =     0.0010
                                                Root MSE          =     .49973

                         (Std. err. adjusted for 3,190 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
            uniedu | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0031608   .0088153     0.36   0.720    -.0141234    .0204451
  gender_candidate |  -.0128609   .0088603    -1.45   0.147    -.0302333    .0045115
   class_candidate |  -.0140097    .008807    -1.59   0.112    -.0312775    .0032582
   rural_candidate |  -.0034081   .0088168    -0.39   0.699    -.0206952     .013879
    policy_welfare |    .004186   .0088515     0.47   0.636    -.0131693    .0215413
policy_immigration |   .0015525   .0089741     0.17   0.863    -.0160432    .0191481
policy_environment |  -.0177807    .008905    -2.00   0.046    -.0352408   -.0003207
      policy_abort |   .0115539   .0088495     1.31   0.192    -.0057973    .0289052
   conflict_appeal |   .0175374   .0121748     1.44   0.150    -.0063339    .0414086
 solidarity_appeal |   .0098778   .0121427     0.81   0.416    -.0139305    .0336862
             _cons |   .4878749   .0178607    27.32   0.000     .4528552    .5228945
------------------------------------------------------------------------------------

. est store m2

. reg rightscale $dimensions_no, cl(RespondentSerial)

Linear regression                               Number of obs     =     11,724
                                                F(10, 2930)       =       1.20
                                                Prob > F          =     0.2834
                                                R-squared         =     0.0010
                                                Root MSE          =     .24247

                         (Std. err. adjusted for 2,931 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
        rightscale | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0006687   .0044143     0.15   0.880    -.0079867    .0093242
  gender_candidate |  -.0088993    .004412    -2.02   0.044    -.0175503   -.0002483
   class_candidate |   .0063862   .0045512     1.40   0.161    -.0025377      .01531
   rural_candidate |   .0012015   .0043415     0.28   0.782    -.0073111    .0097141
    policy_welfare |  -.0068408   .0045355    -1.51   0.132    -.0157338    .0020523
policy_immigration |   .0023835   .0045726     0.52   0.602    -.0065823    .0113493
policy_environment |  -.0042663   .0045454    -0.94   0.348    -.0131788    .0046462
      policy_abort |  -.0047337   .0044136    -1.07   0.284    -.0133878    .0039204
   conflict_appeal |   .0049354   .0060064     0.82   0.411    -.0068418    .0167126
 solidarity_appeal |   .0054416   .0061515     0.88   0.376      -.00662    .0175033
             _cons |   .5110391   .0091597    55.79   0.000     .4930791    .5289991
------------------------------------------------------------------------------------

. est store m3

. 
. coefplot  (m1, label(Male) color(black) ciopts(lcolor(black))) (m2, label(University education)  color(gs8) ciopts(lcolor(gs8))) 
> (m3, label(Rightscale)  color(gs12) ciopts(lcolor(gs12))),  drop(_cons) ylabel(, nogrid)  xline(0, lpat(shortdash_dot)) legend(re
> gion(col(white))cols(3)) plotregion(style(none)) lwidth(vvthin)  ms(O) scale(.8) title("Norway", size(medsmall))

. graph save "balance_no.gph", replace
file balance_no.gph saved

. 
. 
. foreach x in male uniedu rightscale{
  2.         reg `x' $dimensions_no, cl( RespondentSerial )
  3.         test $dimensions_no
  4. }

Linear regression                               Number of obs     =     12,760
                                                F(10, 3189)       =       0.48
                                                Prob > F          =     0.9039
                                                R-squared         =     0.0004
                                                Root MSE          =     .50012

                         (Std. err. adjusted for 3,190 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
              male | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0035573   .0088254    -0.40   0.687    -.0208612    .0137467
  gender_candidate |   .0100597   .0088536     1.14   0.256    -.0072996    .0274189
   class_candidate |   .0075714   .0088116     0.86   0.390    -.0097055    .0248483
   rural_candidate |  -.0083022   .0088195    -0.94   0.347    -.0255946    .0089902
    policy_welfare |  -.0016348   .0088695    -0.18   0.854    -.0190253    .0157556
policy_immigration |   .0003737   .0089784     0.04   0.967    -.0172303    .0179778
policy_environment |   .0111055   .0089081     1.25   0.213    -.0063607    .0285716
      policy_abort |   .0000739   .0088478     0.01   0.993     -.017274    .0174219
   conflict_appeal |  -.0022933   .0121988    -0.19   0.851    -.0262116     .021625
 solidarity_appeal |    .000346    .012183     0.03   0.977    -.0235414    .0242334
             _cons |   .4904204   .0178899    27.41   0.000     .4553436    .5254972
------------------------------------------------------------------------------------

 ( 1)  age_candidate = 0
 ( 2)  gender_candidate = 0
 ( 3)  class_candidate = 0
 ( 4)  rural_candidate = 0
 ( 5)  policy_welfare = 0
 ( 6)  policy_immigration = 0
 ( 7)  policy_environment = 0
 ( 8)  policy_abort = 0
 ( 9)  conflict_appeal = 0
 (10)  solidarity_appeal = 0

       F( 10,  3189) =    0.48
            Prob > F =    0.9039

Linear regression                               Number of obs     =     12,760
                                                F(10, 3189)       =       1.30
                                                Prob > F          =     0.2251
                                                R-squared         =     0.0010
                                                Root MSE          =     .49973

                         (Std. err. adjusted for 3,190 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
            uniedu | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0031608   .0088153     0.36   0.720    -.0141234    .0204451
  gender_candidate |  -.0128609   .0088603    -1.45   0.147    -.0302333    .0045115
   class_candidate |  -.0140097    .008807    -1.59   0.112    -.0312775    .0032582
   rural_candidate |  -.0034081   .0088168    -0.39   0.699    -.0206952     .013879
    policy_welfare |    .004186   .0088515     0.47   0.636    -.0131693    .0215413
policy_immigration |   .0015525   .0089741     0.17   0.863    -.0160432    .0191481
policy_environment |  -.0177807    .008905    -2.00   0.046    -.0352408   -.0003207
      policy_abort |   .0115539   .0088495     1.31   0.192    -.0057973    .0289052
   conflict_appeal |   .0175374   .0121748     1.44   0.150    -.0063339    .0414086
 solidarity_appeal |   .0098778   .0121427     0.81   0.416    -.0139305    .0336862
             _cons |   .4878749   .0178607    27.32   0.000     .4528552    .5228945
------------------------------------------------------------------------------------

 ( 1)  age_candidate = 0
 ( 2)  gender_candidate = 0
 ( 3)  class_candidate = 0
 ( 4)  rural_candidate = 0
 ( 5)  policy_welfare = 0
 ( 6)  policy_immigration = 0
 ( 7)  policy_environment = 0
 ( 8)  policy_abort = 0
 ( 9)  conflict_appeal = 0
 (10)  solidarity_appeal = 0

       F( 10,  3189) =    1.30
            Prob > F =    0.2251

Linear regression                               Number of obs     =     11,724
                                                F(10, 2930)       =       1.20
                                                Prob > F          =     0.2834
                                                R-squared         =     0.0010
                                                Root MSE          =     .24247

                         (Std. err. adjusted for 2,931 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
        rightscale | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0006687   .0044143     0.15   0.880    -.0079867    .0093242
  gender_candidate |  -.0088993    .004412    -2.02   0.044    -.0175503   -.0002483
   class_candidate |   .0063862   .0045512     1.40   0.161    -.0025377      .01531
   rural_candidate |   .0012015   .0043415     0.28   0.782    -.0073111    .0097141
    policy_welfare |  -.0068408   .0045355    -1.51   0.132    -.0157338    .0020523
policy_immigration |   .0023835   .0045726     0.52   0.602    -.0065823    .0113493
policy_environment |  -.0042663   .0045454    -0.94   0.348    -.0131788    .0046462
      policy_abort |  -.0047337   .0044136    -1.07   0.284    -.0133878    .0039204
   conflict_appeal |   .0049354   .0060064     0.82   0.411    -.0068418    .0167126
 solidarity_appeal |   .0054416   .0061515     0.88   0.376      -.00662    .0175033
             _cons |   .5110391   .0091597    55.79   0.000     .4930791    .5289991
------------------------------------------------------------------------------------

 ( 1)  age_candidate = 0
 ( 2)  gender_candidate = 0
 ( 3)  class_candidate = 0
 ( 4)  rural_candidate = 0
 ( 5)  policy_welfare = 0
 ( 6)  policy_immigration = 0
 ( 7)  policy_environment = 0
 ( 8)  policy_abort = 0
 ( 9)  conflict_appeal = 0
 (10)  solidarity_appeal = 0

       F( 10,  2930) =    1.20
            Prob > F =    0.2834

. 
. 
. clear 

. use "Data_Britain.dta"

. 
. reg male $dimensions_uk, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(10, 2000)       =       2.03
                                                Prob > F          =     0.0268
                                                R-squared         =     0.0025
                                                Root MSE          =     .49649

                         (Std. err. adjusted for 2,001 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
              male | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0027606   .0110268     0.25   0.802    -.0188646    .0243858
  gender_candidate |  -.0068812   .0111421    -0.62   0.537    -.0287325    .0149701
   class_candidate |  -.0039052   .0108964    -0.36   0.720    -.0252747    .0174644
   rural_candidate |   -.008039   .0113302    -0.71   0.478    -.0302593    .0141812
    policy_welfare |   .0213232    .011016     1.94   0.053    -.0002808    .0429272
policy_immigration |   .0216443   .0112234     1.93   0.054    -.0003665    .0436551
policy_environment |   .0086087   .0109667     0.78   0.433    -.0128987    .0301162
         policy_eu |   .0259853   .0109217     2.38   0.017     .0045662    .0474044
   conflict_appeal |   .0020046   .0152153     0.13   0.895    -.0278348    .0318441
 solidarity_appeal |  -.0255771   .0149506    -1.71   0.087    -.0548975    .0037433
             _cons |   .4220747   .0216396    19.50   0.000     .3796362    .4645131
------------------------------------------------------------------------------------

. est store m1

. reg voted $dimensions_uk, cl(RespondentSerial)

Linear regression                               Number of obs     =      7,904
                                                F(10, 1975)       =       0.82
                                                Prob > F          =     0.6132
                                                R-squared         =     0.0010
                                                Root MSE          =     .40704

                         (Std. err. adjusted for 1,976 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
             voted | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0072078    .008863    -0.81   0.416    -.0245895     .010174
  gender_candidate |   .0008908    .009223     0.10   0.923    -.0171971    .0189787
   class_candidate |  -.0017587   .0089111    -0.20   0.844    -.0192348    .0157174
   rural_candidate |  -.0064373   .0091607    -0.70   0.482    -.0244029    .0115283
    policy_welfare |   .0018842    .008923     0.21   0.833    -.0156153    .0193836
policy_immigration |  -.0067902   .0095886    -0.71   0.479    -.0255951    .0120148
policy_environment |  -.0158415   .0088211    -1.80   0.073    -.0331411     .001458
         policy_eu |    .008384   .0087391     0.96   0.337    -.0087548    .0255227
   conflict_appeal |   .0161693    .012596     1.28   0.199    -.0085335    .0408721
 solidarity_appeal |   .0189116   .0122625     1.54   0.123    -.0051372    .0429604
             _cons |   .7899207   .0175334    45.05   0.000     .7555347    .8243066
------------------------------------------------------------------------------------

. est store m2

. reg aged $dimensions_uk, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(10, 2000)       =       0.52
                                                Prob > F          =     0.8736
                                                R-squared         =     0.0006
                                                Root MSE          =     .22033

                         (Std. err. adjusted for 2,001 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
              aged | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0013091   .0048731    -0.27   0.788     -.010866    .0082479
  gender_candidate |   .0004849   .0049485     0.10   0.922    -.0092199    .0101896
   class_candidate |  -.0040377   .0047002    -0.86   0.390    -.0132554      .00518
   rural_candidate |  -.0017562   .0050086    -0.35   0.726    -.0115788    .0080663
    policy_welfare |   .0027402   .0048516     0.56   0.572    -.0067745    .0122549
policy_immigration |   .0019776   .0050252     0.39   0.694    -.0078775    .0118327
policy_environment |  -.0010467   .0047722    -0.22   0.826    -.0104056    .0083123
         policy_eu |   .0093421   .0049236     1.90   0.058    -.0003138     .018998
   conflict_appeal |  -.0024366   .0069253    -0.35   0.725    -.0160181    .0111449
 solidarity_appeal |  -.0007721   .0067818    -0.11   0.909    -.0140723     .012528
             _cons |   .4238147    .009715    43.62   0.000     .4047622    .4428673
------------------------------------------------------------------------------------

. est store m3

. 
. coefplot  (m1, label(Male) color(black) ciopts(lcolor(black))) (m2, label(Turnout)  color(gs8) ciopts(lcolor(gs8))) (m3, label(Ag
> e)  color(gs12) ciopts(lcolor(gs12))),  drop(_cons) ylabel(, nogrid)  xline(0, lpat(shortdash_dot)) legend(region(col(white)) col
> s(3)) plotregion(style(none)) lwidth(vvthin)  ms(O) scale(.8) title("Britain", size(medsmall))

. graph save "balance_uk.gph", replace
file balance_uk.gph saved

. 
. 
. foreach x in male voted aged{
  2.         reg `x' $dimensions_uk, cl( RespondentSerial )
  3.         test $dimensions_uk
  4. }

Linear regression                               Number of obs     =      8,004
                                                F(10, 2000)       =       2.03
                                                Prob > F          =     0.0268
                                                R-squared         =     0.0025
                                                Root MSE          =     .49649

                         (Std. err. adjusted for 2,001 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
              male | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0027606   .0110268     0.25   0.802    -.0188646    .0243858
  gender_candidate |  -.0068812   .0111421    -0.62   0.537    -.0287325    .0149701
   class_candidate |  -.0039052   .0108964    -0.36   0.720    -.0252747    .0174644
   rural_candidate |   -.008039   .0113302    -0.71   0.478    -.0302593    .0141812
    policy_welfare |   .0213232    .011016     1.94   0.053    -.0002808    .0429272
policy_immigration |   .0216443   .0112234     1.93   0.054    -.0003665    .0436551
policy_environment |   .0086087   .0109667     0.78   0.433    -.0128987    .0301162
         policy_eu |   .0259853   .0109217     2.38   0.017     .0045662    .0474044
   conflict_appeal |   .0020046   .0152153     0.13   0.895    -.0278348    .0318441
 solidarity_appeal |  -.0255771   .0149506    -1.71   0.087    -.0548975    .0037433
             _cons |   .4220747   .0216396    19.50   0.000     .3796362    .4645131
------------------------------------------------------------------------------------

 ( 1)  age_candidate = 0
 ( 2)  gender_candidate = 0
 ( 3)  class_candidate = 0
 ( 4)  rural_candidate = 0
 ( 5)  policy_welfare = 0
 ( 6)  policy_immigration = 0
 ( 7)  policy_environment = 0
 ( 8)  policy_eu = 0
 ( 9)  conflict_appeal = 0
 (10)  solidarity_appeal = 0

       F( 10,  2000) =    2.03
            Prob > F =    0.0268

Linear regression                               Number of obs     =      7,904
                                                F(10, 1975)       =       0.82
                                                Prob > F          =     0.6132
                                                R-squared         =     0.0010
                                                Root MSE          =     .40704

                         (Std. err. adjusted for 1,976 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
             voted | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0072078    .008863    -0.81   0.416    -.0245895     .010174
  gender_candidate |   .0008908    .009223     0.10   0.923    -.0171971    .0189787
   class_candidate |  -.0017587   .0089111    -0.20   0.844    -.0192348    .0157174
   rural_candidate |  -.0064373   .0091607    -0.70   0.482    -.0244029    .0115283
    policy_welfare |   .0018842    .008923     0.21   0.833    -.0156153    .0193836
policy_immigration |  -.0067902   .0095886    -0.71   0.479    -.0255951    .0120148
policy_environment |  -.0158415   .0088211    -1.80   0.073    -.0331411     .001458
         policy_eu |    .008384   .0087391     0.96   0.337    -.0087548    .0255227
   conflict_appeal |   .0161693    .012596     1.28   0.199    -.0085335    .0408721
 solidarity_appeal |   .0189116   .0122625     1.54   0.123    -.0051372    .0429604
             _cons |   .7899207   .0175334    45.05   0.000     .7555347    .8243066
------------------------------------------------------------------------------------

 ( 1)  age_candidate = 0
 ( 2)  gender_candidate = 0
 ( 3)  class_candidate = 0
 ( 4)  rural_candidate = 0
 ( 5)  policy_welfare = 0
 ( 6)  policy_immigration = 0
 ( 7)  policy_environment = 0
 ( 8)  policy_eu = 0
 ( 9)  conflict_appeal = 0
 (10)  solidarity_appeal = 0

       F( 10,  1975) =    0.82
            Prob > F =    0.6132

Linear regression                               Number of obs     =      8,004
                                                F(10, 2000)       =       0.52
                                                Prob > F          =     0.8736
                                                R-squared         =     0.0006
                                                Root MSE          =     .22033

                         (Std. err. adjusted for 2,001 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
              aged | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0013091   .0048731    -0.27   0.788     -.010866    .0082479
  gender_candidate |   .0004849   .0049485     0.10   0.922    -.0092199    .0101896
   class_candidate |  -.0040377   .0047002    -0.86   0.390    -.0132554      .00518
   rural_candidate |  -.0017562   .0050086    -0.35   0.726    -.0115788    .0080663
    policy_welfare |   .0027402   .0048516     0.56   0.572    -.0067745    .0122549
policy_immigration |   .0019776   .0050252     0.39   0.694    -.0078775    .0118327
policy_environment |  -.0010467   .0047722    -0.22   0.826    -.0104056    .0083123
         policy_eu |   .0093421   .0049236     1.90   0.058    -.0003138     .018998
   conflict_appeal |  -.0024366   .0069253    -0.35   0.725    -.0160181    .0111449
 solidarity_appeal |  -.0007721   .0067818    -0.11   0.909    -.0140723     .012528
             _cons |   .4238147    .009715    43.62   0.000     .4047622    .4428673
------------------------------------------------------------------------------------

 ( 1)  age_candidate = 0
 ( 2)  gender_candidate = 0
 ( 3)  class_candidate = 0
 ( 4)  rural_candidate = 0
 ( 5)  policy_welfare = 0
 ( 6)  policy_immigration = 0
 ( 7)  policy_environment = 0
 ( 8)  policy_eu = 0
 ( 9)  conflict_appeal = 0
 (10)  solidarity_appeal = 0

       F( 10,  2000) =    0.52
            Prob > F =    0.8736

. 
.  
. gr combine "balance_no" "balance_uk", xcommon  imargin(small)

. gr save "balance_fig.gph", replace
file balance_fig.gph saved

. gr export  "figureA8.pdf",as(pdf) replace
file figureA8.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *FIGURE A9**********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg selected $dimensions_no if male==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      6,178
                                                F(10, 1572)       =       5.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0084
                                                Root MSE          =     .49834

                         (Std. err. adjusted for 1,573 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0121082   .0125826    -0.96   0.336    -.0367886    .0125723
  gender_candidate |  -.0292725   .0125716    -2.33   0.020    -.0539313   -.0046138
   class_candidate |  -.0073289   .0128977    -0.57   0.570    -.0326274    .0179697
   rural_candidate |   .0058319   .0125987     0.46   0.644    -.0188801     .030544
    policy_welfare |   .0774601   .0129328     5.99   0.000     .0520928    .1028274
policy_immigration |  -.0240763    .012884    -1.87   0.062    -.0493479    .0011953
policy_environment |  -.0180002   .0131465    -1.37   0.171    -.0437867    .0077863
      policy_abort |  -.0004797   .0128728    -0.04   0.970    -.0257294    .0247699
   conflict_appeal |  -.0112747   .0181112    -0.62   0.534    -.0467993      .02425
 solidarity_appeal |  -.0288006   .0180141    -1.60   0.110    -.0641348    .0065335
             _cons |   .5194861   .0224616    23.13   0.000     .4754283    .5635439
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_no if male==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      6,126
                                                F(10, 1563)       =       2.32
                                                Prob > F          =     0.0103
                                                R-squared         =     0.0041
                                                Root MSE          =     .49943

                         (Std. err. adjusted for 1,564 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0200748   .0129004    -1.56   0.120    -.0453786    .0052291
  gender_candidate |  -.0038804   .0124441    -0.31   0.755    -.0282892    .0205285
   class_candidate |  -.0065096    .013091    -0.50   0.619    -.0321873    .0191682
   rural_candidate |   .0166356   .0130883     1.27   0.204    -.0090369    .0423081
    policy_welfare |   .0198745   .0133927     1.48   0.138    -.0063951    .0461441
policy_immigration |   .0506754   .0132475     3.83   0.000     .0246906    .0766602
policy_environment |   .0142018    .013027     1.09   0.276    -.0113504    .0397541
      policy_abort |  -.0021135   .0129911    -0.16   0.871    -.0275954    .0233684
   conflict_appeal |  -.0146575   .0182614    -0.80   0.422     -.050477    .0211619
 solidarity_appeal |  -.0079478   .0177486    -0.45   0.654    -.0427613    .0268657
             _cons |   .4742926   .0226142    20.97   0.000     .4299352      .51865
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Female) color(black)) (wc1, label(Male)),  drop(_cons) ylabel(, nogrid) xsc(r(-.15 .15)) xlab(-.15 (.1) .15
> ) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("AMCE by 
> gender", size(medsmall))

. graph save "amce_male_no.gph", replace
file amce_male_no.gph saved

.  
.  
. global dimensions_int_male age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration polic
> y_environment policy_abort  conflict_appeal solidarity_appeal age_candidateXmale gender_candidateXmale class_candidateXmale rural
> _candidateXmale policy_welfareXmale policy_immigrationXmale policy_environmentXmale policy_abortXmale conflict_appealXmale solida
> rity_appealXmale

. 
. reg selected $dimensions_int_male male, cl(RespondentSerial)

Linear regression                               Number of obs     =     12,304
                                                F(21, 3136)       =       3.65
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0062
                                                Root MSE          =     .49888

                              (Std. err. adjusted for 3,137 clusters in RespondentSerial)
-----------------------------------------------------------------------------------------
                        |               Robust
               selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
          age_candidate |  -.0121082   .0125812    -0.96   0.336    -.0367763      .01256
       gender_candidate |  -.0292725   .0125701    -2.33   0.020     -.053919   -.0046261
        class_candidate |  -.0073289   .0128962    -0.57   0.570    -.0326148     .017957
        rural_candidate |   .0058319   .0125973     0.46   0.643    -.0188678    .0305316
         policy_welfare |   .0774601   .0129313     5.99   0.000     .0521055    .1028147
     policy_immigration |  -.0240763   .0128825    -1.87   0.062    -.0493353    .0011827
     policy_environment |  -.0180002    .013145    -1.37   0.171    -.0437738    .0077735
           policy_abort |  -.0004797   .0128713    -0.04   0.970    -.0257168    .0247573
        conflict_appeal |  -.0112747   .0181091    -0.62   0.534    -.0467816    .0242323
      solidarity_appeal |  -.0288006    .018012    -1.60   0.110    -.0641171    .0065159
     age_candidateXmale |  -.0079666   .0180184    -0.44   0.658    -.0432957    .0273625
  gender_candidateXmale |   .0253922   .0176868     1.44   0.151    -.0092868    .0600712
   class_candidateXmale |   .0008193   .0183751     0.04   0.964    -.0352092    .0368478
   rural_candidateXmale |   .0108037   .0181646     0.59   0.552     -.024812    .0464194
    policy_welfareXmale |  -.0575856   .0186156    -3.09   0.002    -.0940855   -.0210857
policy_immigrationXmale |   .0747517   .0184773     4.05   0.000     .0385228    .1109806
policy_environmentXmale |    .032202   .0185054     1.74   0.082     -.004082     .068486
      policy_abortXmale |  -.0016337   .0182866    -0.09   0.929    -.0374886    .0342211
   conflict_appealXmale |  -.0033829   .0257165    -0.13   0.895    -.0538057    .0470399
 solidarity_appealXmale |   .0208528   .0252857     0.82   0.410    -.0287254     .070431
                   male |  -.0451935   .0318698    -1.42   0.156    -.1076813    .0172942
                  _cons |   .5194861    .022459    23.13   0.000     .4754503    .5635219
-----------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_no male) ylabel(, nogrid) xsc(r(-.15 .1)) xlab(-.15 (.1) .1) xline(0, lpat(shortdash_do
> t)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by gender", size(medsmall)) 

. graph save "amce_male_diff_no.gph", replace
file amce_male_diff_no.gph saved

. 
. clear 

. use "Data_Britain.dta"

. 
. reg selected $dimensions_uk if male==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      4,456
                                                F(10, 1113)       =      11.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0246
                                                Root MSE          =     .49442

                         (Std. err. adjusted for 1,114 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0267877   .0151865    -1.76   0.078     -.056585    .0030096
  gender_candidate |  -.0367542   .0146089    -2.52   0.012    -.0654183   -.0080902
   class_candidate |   .0068648   .0148062     0.46   0.643    -.0221864    .0359159
   rural_candidate |   .0065731   .0151098     0.44   0.664    -.0230739    .0362201
    policy_welfare |   .0538507    .014923     3.61   0.000     .0245703    .0831311
policy_immigration |   .0732239   .0153283     4.78   0.000     .0431482    .1032996
policy_environment |  -.0993949   .0152213    -6.53   0.000    -.1292606   -.0695292
         policy_eu |  -.0442522    .015416    -2.87   0.004       -.0745   -.0140044
   conflict_appeal |   .0189198   .0212682     0.89   0.374    -.0228104    .0606501
 solidarity_appeal |  -.0337555   .0212409    -1.59   0.112    -.0754323    .0079213
             _cons |   .5398827   .0261522    20.64   0.000     .4885695    .5911958
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_uk if male==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      3,548
                                                F(10, 886)        =       6.23
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0179
                                                Root MSE          =     .49627

                           (Std. err. adjusted for 887 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |    .003447   .0171844     0.20   0.841    -.0302799    .0371739
  gender_candidate |   -.020651   .0167234    -1.23   0.217    -.0534731    .0121712
   class_candidate |   .0326372   .0174285     1.87   0.061    -.0015687    .0668432
   rural_candidate |  -.0133812   .0168281    -0.80   0.427    -.0464087    .0196464
    policy_welfare |   .0360397   .0179794     2.00   0.045     .0007525     .071327
policy_immigration |   .0910116   .0175493     5.19   0.000     .0565685    .1254548
policy_environment |  -.0680508    .017843    -3.81   0.000    -.1030703   -.0330312
         policy_eu |  -.0338415   .0177286    -1.91   0.057    -.0686364    .0009535
   conflict_appeal |  -.0433421   .0231713    -1.87   0.062    -.0888191     .002135
 solidarity_appeal |  -.0413111   .0238924    -1.73   0.084    -.0882033    .0055811
             _cons |   .5196403   .0305475    17.01   0.000     .4596864    .5795943
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Female) color(black)) (wc1, label(Male)),  drop(_cons) ylabel(, nogrid) xsc(r(-.15 .15)) xlab(-.15 (.1) .15
> )  xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("AMCE by
>  gender", size(medsmall))

. graph save "amce_male_uk.gph", replace
file amce_male_uk.gph saved

. 
. global dimensions_int_male age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration polic
> y_environment policy_eu conflict_appeal solidarity_appeal age_candidateXmale gender_candidateXmale class_candidateXmale rural_can
> didateXmale policy_welfareXmale policy_immigrationXmale policy_environmentXmale policy_euXmale  conflict_appealXmale solidarity_a
> ppealXmale

. 
. reg selected $dimensions_int_male male, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(21, 2000)       =       8.64
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0217
                                                Root MSE          =     .49524

                              (Std. err. adjusted for 2,001 clusters in RespondentSerial)
-----------------------------------------------------------------------------------------
                        |               Robust
               selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
          age_candidate |  -.0267877   .0151863    -1.76   0.078    -.0565704     .002995
       gender_candidate |  -.0367542   .0146087    -2.52   0.012    -.0654042   -.0081043
        class_candidate |   .0068648    .014806     0.46   0.643    -.0221721    .0359016
        rural_candidate |   .0065731   .0151097     0.44   0.664    -.0230593    .0362055
         policy_welfare |   .0538507   .0149229     3.61   0.000     .0245846    .0831167
     policy_immigration |   .0732239   .0153282     4.78   0.000      .043163    .1032848
     policy_environment |  -.0993949   .0152212    -6.53   0.000     -.129246   -.0695439
              policy_eu |  -.0442522   .0154159    -2.87   0.004    -.0744851   -.0140193
        conflict_appeal |   .0189198    .021268     0.89   0.374    -.0227899    .0606296
      solidarity_appeal |  -.0337555   .0212408    -1.59   0.112    -.0754118    .0079008
     age_candidateXmale |   .0302347   .0229278     1.32   0.187    -.0147302    .0751996
  gender_candidateXmale |   .0161033   .0222004     0.73   0.468    -.0274351    .0596416
   class_candidateXmale |   .0257725   .0228631     1.13   0.260    -.0190655    .0706104
   rural_candidateXmale |  -.0199543   .0226109    -0.88   0.378    -.0642977    .0243892
    policy_welfareXmale |  -.0178109   .0233599    -0.76   0.446    -.0636233    .0280014
policy_immigrationXmale |   .0177878   .0232955     0.76   0.445    -.0278982    .0634737
policy_environmentXmale |   .0313442   .0234477     1.34   0.181    -.0146404    .0773287
         policy_euXmale |   .0104108   .0234882     0.44   0.658    -.0356531    .0564746
   conflict_appealXmale |  -.0622619   .0314451    -1.98   0.048    -.1239305   -.0005933
 solidarity_appealXmale |  -.0075556   .0319616    -0.24   0.813    -.0702371    .0551259
                   male |  -.0202423   .0402033    -0.50   0.615    -.0990871    .0586025
                  _cons |   .5398827   .0261519    20.64   0.000     .4885948    .5911706
-----------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_uk male) ylabel(, nogrid) xsc(r(-.15 .1)) xlab(-.15 (.1) .1) xline(0, lpat(shortdash_do
> t)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by gender", size(medsmall)) 

. graph save "amce_male_diff_uk.gph", replace
file amce_male_diff_uk.gph saved

. 
. gr combine "amce_male_no.gph" "amce_male_diff_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "male_no.gph", replace
file male_no.gph saved

. gr combine "amce_male_uk.gph" "amce_male_diff_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "male_uk.gph", replace
file male_uk.gph saved

. grc1leg2 "male_no.gph" "male_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA9.gph", replace
file figureA9.gph saved

. gr export  "figureA9.pdf",as(pdf) replace
file figureA9.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *FIGURE A10*********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg selected $dimensions_no if older==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      5,100
                                                F(10, 1296)       =       2.55
                                                Prob > F          =     0.0048
                                                R-squared         =     0.0049
                                                Root MSE          =      .4993

                         (Std. err. adjusted for 1,297 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0128479   .0139934    -0.92   0.359    -.0403001    .0146044
  gender_candidate |  -.0257305   .0135609    -1.90   0.058    -.0523342    .0008733
   class_candidate |   .0040155   .0143149     0.28   0.779    -.0240673    .0320983
   rural_candidate |   .0375802   .0139223     2.70   0.007     .0102674    .0648929
    policy_welfare |   .0462415    .014192     3.26   0.001     .0183996    .0740833
policy_immigration |   .0076912   .0148029     0.52   0.603    -.0213491    .0367315
policy_environment |  -.0167627   .0143148    -1.17   0.242    -.0448455      .01132
      policy_abort |   .0120961   .0137621     0.88   0.380    -.0149023    .0390946
   conflict_appeal |  -.0089852   .0202521    -0.44   0.657    -.0487157    .0307452
 solidarity_appeal |   -.010619   .0198771    -0.53   0.593    -.0496138    .0283757
             _cons |   .4812061   .0255612    18.83   0.000     .4310603     .531352
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_no if older==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      7,204
                                                F(10, 1839)       =       2.90
                                                Prob > F          =     0.0013
                                                R-squared         =     0.0041
                                                Root MSE          =     .49936

                         (Std. err. adjusted for 1,840 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0192374   .0118215    -1.63   0.104    -.0424224    .0039476
  gender_candidate |  -.0095869   .0116972    -0.82   0.413     -.032528    .0133543
   class_candidate |  -.0149616   .0120225    -1.24   0.213    -.0385408    .0086176
   rural_candidate |  -.0079064    .011993    -0.66   0.510    -.0314278    .0156149
    policy_welfare |   .0500196     .01237     4.04   0.000      .025759    .0742802
policy_immigration |   .0172127   .0118956     1.45   0.148    -.0061176     .040543
policy_environment |   .0081402    .012145     0.67   0.503    -.0156793    .0319597
      policy_abort |  -.0110906   .0122256    -0.91   0.364    -.0350682    .0128869
   conflict_appeal |  -.0155223   .0167324    -0.93   0.354    -.0483388    .0172942
 solidarity_appeal |  -.0246217   .0164161    -1.50   0.134     -.056818    .0075745
             _cons |   .5096396   .0204991    24.86   0.000     .4694356    .5498436
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Young) color(black)) (wc1, label(Old)),  drop(_cons) ylabel(, nogrid) xsc(r(-.2 .2)) xlab(-.2 (.1) .2) xlin
> e(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("AMCE by age", 
> size(medsmall))

. graph save "amce_age_no.gph", replace
file amce_age_no.gph saved

. 
. global dimensions_int_old age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration policy
> _environment policy_abort  conflict_appeal solidarity_appeal age_candidateXold gender_candidateXold class_candidateXold rural_can
> didateXold policy_welfareXold policy_immigrationXold policy_environmentXold policy_abortXold conflict_appealXold solidarity_appea
> lXold

. 
. reg selected $dimensions_int_old older, cl(RespondentSerial)

Linear regression                               Number of obs     =     12,304
                                                F(21, 3136)       =       2.60
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0044
                                                Root MSE          =     .49934

                             (Std. err. adjusted for 3,137 clusters in RespondentSerial)
----------------------------------------------------------------------------------------
                       |               Robust
              selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
         age_candidate |  -.0128479   .0139885    -0.92   0.358    -.0402754    .0145796
      gender_candidate |  -.0257305   .0135561    -1.90   0.058    -.0523103    .0008493
       class_candidate |   .0040155   .0143098     0.28   0.779     -.024042     .032073
       rural_candidate |   .0375802   .0139174     2.70   0.007      .010292    .0648683
        policy_welfare |   .0462415    .014187     3.26   0.001     .0184247    .0740582
    policy_immigration |   .0076912   .0147977     0.52   0.603     -.021323    .0367053
    policy_environment |  -.0167627   .0143098    -1.17   0.242    -.0448202    .0112947
          policy_abort |   .0120961   .0137572     0.88   0.379    -.0148779    .0390702
       conflict_appeal |  -.0089852   .0202449    -0.44   0.657    -.0486798    .0307094
     solidarity_appeal |   -.010619   .0198701    -0.53   0.593    -.0495787    .0283406
     age_candidateXold |  -.0063895    .018315    -0.35   0.727    -.0423001    .0295211
  gender_candidateXold |   .0161436   .0179055     0.90   0.367     -.018964    .0512512
   class_candidateXold |  -.0189771   .0186902    -1.02   0.310    -.0556234    .0176692
   rural_candidateXold |  -.0454866   .0183723    -2.48   0.013    -.0815094   -.0094637
    policy_welfareXold |   .0037781   .0188229     0.20   0.841    -.0331283    .0406845
policy_immigrationXold |   .0095215   .0189866     0.50   0.616    -.0277059    .0467489
policy_environmentXold |   .0249029   .0187693     1.33   0.185    -.0118983    .0617042
      policy_abortXold |  -.0231868   .0184049    -1.26   0.208    -.0592737    .0129001
   conflict_appealXold |  -.0065371   .0262651    -0.25   0.803    -.0580356    .0449615
 solidarity_appealXold |  -.0140027   .0257747    -0.54   0.587    -.0645396    .0365342
                 older |   .0284335   .0327592     0.87   0.385    -.0357982    .0926652
                 _cons |   .4812061   .0255522    18.83   0.000     .4311055    .5313068
----------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_no older) ylabel(, nogrid) xsc(r(-.1 .2)) xlab(-.1 (.1) .2) xline(0, lpat(shortdash_dot
> )) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by age", size(medsmall)) 

. graph save "amce_age_diff_no.gph", replace
file amce_age_diff_no.gph saved

. 
. clear 

. use "Data_Britain.dta"

. 
. reg selected $dimensions_uk if older==0, cl(RespondentSerial)

Linear regression                               Number of obs     =      3,180
                                                F(10, 794)        =       7.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0263
                                                Root MSE          =     .49425

                           (Std. err. adjusted for 795 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0163219   .0178891    -0.91   0.362    -.0514374    .0187936
  gender_candidate |  -.0368166   .0176675    -2.08   0.037    -.0714971   -.0021361
   class_candidate |   .0262209    .017846     1.47   0.142      -.00881    .0612519
   rural_candidate |  -.0118043   .0176053    -0.67   0.503    -.0463627    .0227541
    policy_welfare |   .0249949   .0181404     1.38   0.169    -.0106139    .0606038
policy_immigration |   .0163617   .0177972     0.92   0.358    -.0185735    .0512969
policy_environment |  -.1146898   .0185048    -6.20   0.000    -.1510139   -.0783657
         policy_eu |  -.0888301   .0177124    -5.02   0.000    -.1235987   -.0540614
   conflict_appeal |   .0224375   .0242148     0.93   0.354    -.0250951    .0699702
 solidarity_appeal |   -.027368   .0244005    -1.12   0.262    -.0752651    .0205291
             _cons |   .6010959   .0309692    19.41   0.000     .5403047     .661887
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_uk if older==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      4,824
                                                F(10, 1205)       =      12.20
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0253
                                                Root MSE          =      .4942

                         (Std. err. adjusted for 1,206 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0101426   .0146928    -0.69   0.490    -.0389689    .0186837
  gender_candidate |  -.0253289   .0140406    -1.80   0.071    -.0528757     .002218
   class_candidate |   .0140114   .0146189     0.96   0.338    -.0146699    .0426926
   rural_candidate |   .0023964   .0145873     0.16   0.870     -.026223    .0310158
    policy_welfare |    .059547   .0149135     3.99   0.000     .0302877    .0888064
policy_immigration |    .124945   .0150245     8.32   0.000     .0954679    .1544222
policy_environment |  -.0649198   .0148548    -4.37   0.000     -.094064   -.0357756
         policy_eu |  -.0066894   .0152516    -0.44   0.661     -.036612    .0232332
   conflict_appeal |  -.0284277   .0204876    -1.39   0.166    -.0686231    .0117677
 solidarity_appeal |  -.0434227   .0207326    -2.09   0.036    -.0840987   -.0027467
             _cons |   .4810737   .0257694    18.67   0.000     .4305157    .5316316
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Young) color(black)) (wc1, label(Old)),  drop(_cons) ylabel(, nogrid) xsc(r(-.2 .2)) xlab(-.2 (.1) .2) xlin
> e(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("AMCE by age", 
> size(medsmall))

. graph save "amce_age_uk.gph", replace
file amce_age_uk.gph saved

. 
. global dimensions_int_old age_candidate gender_candidate class_candidate rural_candidate policy_welfare policy_immigration policy
> _environment policy_eu conflict_appeal solidarity_appeal age_candidateXold gender_candidateXold class_candidateXold rural_candida
> teXold policy_welfareXold policy_immigrationXold policy_environmentXold policy_euXold conflict_appealXmale solidarity_appealXmale

.  
. reg selected $dimensions_int_old older, cl(RespondentSerial)

Linear regression                               Number of obs     =      8,004
                                                F(21, 2000)       =      10.03
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0259
                                                Root MSE          =     .49416

                             (Std. err. adjusted for 2,001 clusters in RespondentSerial)
----------------------------------------------------------------------------------------
                       |               Robust
              selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
         age_candidate |  -.0172724   .0178611    -0.97   0.334    -.0523007    .0177559
      gender_candidate |  -.0376509   .0176488    -2.13   0.033    -.0722628   -.0030389
       class_candidate |   .0251887   .0177734     1.42   0.157    -.0096677    .0600451
       rural_candidate |  -.0115101   .0175777    -0.65   0.513    -.0459827    .0229625
        policy_welfare |   .0253781    .018109     1.40   0.161    -.0101363    .0608924
    policy_immigration |   .0169228   .0177684     0.95   0.341    -.0179238    .0517694
    policy_environment |  -.1138905   .0184675    -6.17   0.000     -.150108   -.0776729
             policy_eu |  -.0886676   .0176624    -5.02   0.000    -.1233062   -.0540289
       conflict_appeal |   .0076061   .0169677     0.45   0.654      -.02567    .0408823
     solidarity_appeal |  -.0457672   .0168474    -2.72   0.007    -.0788074    -.012727
     age_candidateXold |   .0072536    .023133     0.31   0.754    -.0381137    .0526208
  gender_candidateXold |   .0118988   .0225658     0.53   0.598    -.0323561    .0561538
   class_candidateXold |  -.0109994   .0230201    -0.48   0.633    -.0561453    .0341466
   rural_candidateXold |   .0136415   .0228332     0.60   0.550    -.0311378    .0584208
    policy_welfareXold |   .0342632   .0234511     1.46   0.144    -.0117279    .0802542
policy_immigrationXold |   .1078428   .0232739     4.63   0.000     .0621993    .1534864
policy_environmentXold |   .0489989    .023704     2.07   0.039     .0025117    .0954861
         policy_euXold |   .0823003   .0233434     3.53   0.000     .0365204    .1280802
  conflict_appealXmale |   -.035589   .0143415    -2.48   0.013    -.0637148   -.0074632
solidarity_appealXmale |   .0193348   .0144931     1.33   0.182    -.0090883    .0477579
                 older |  -.1463089   .0324306    -4.51   0.000    -.2099102   -.0827077
                 _cons |   .6175098   .0273912    22.54   0.000     .5637914    .6712281
----------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_uk older) ylabel(, nogrid) xsc(r(-.1 .2)) xlab(-.1 (.1) .2) xline(0, lpat(shortdash_dot
> )) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by age", size(medsmall)) 

. graph save "amce_age_diff_uk.gph", replace
file amce_age_diff_uk.gph saved

. 
. gr combine "amce_age_no.gph" "amce_age_diff_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "age_no.gph", replace
file age_no.gph saved

. gr combine "amce_age_uk.gph" "amce_age_diff_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "age_uk.gph", replace
file age_uk.gph saved

. grc1leg2 "age_no.gph" "age_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA10.gph", replace
file figureA10.gph saved

. gr export  "figureA10.pdf",as(pdf) replace
file figureA10.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *TABLE A3***********************************************************************
. ********************************************************************************
. *The code produces the estimates for Total effect (TE); Direct effect (NDE); Indirect effect (NIE) as reported in Table A3
. 
. clear

. use "Data_Norway.dta"

. 
. global cdim age_candidate gender_candidate   rural_candidate   policy_immigration policy_environment policy_abort 

. 
. mediate (selected $cdim class_candidate policy_welfare)(represented $cdim class_candidate policy_welfare) (class_appeal2) if work
> ingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  6.682e-30  
Iteration 1:  EE criterion =  2.078e-31  

Causal mediation analysis                                Number of obs = 2,532

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Multivalued
                                               (Std. err. adjusted for 651 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------------
                                       |               Robust
                              selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
NIE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0024283    .005448     0.45   0.656    -.0082497    .0131062
(Solidarity appeal vs Turnout appeal)  |  -.0058152   .0051067    -1.14   0.255    -.0158241    .0041937
---------------------------------------+----------------------------------------------------------------
NDE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0377693   .0276181     1.37   0.171    -.0163613    .0918999
(Solidarity appeal vs Turnout appeal)  |   .0115395   .0267343     0.43   0.666    -.0408588    .0639378
---------------------------------------+----------------------------------------------------------------
TE                                     |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0401976   .0282497     1.42   0.155    -.0151707    .0955659
(Solidarity appeal vs Turnout appeal)  |   .0057243   .0272822     0.21   0.834    -.0477478    .0591964
--------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim class_candidate policy_welfare)(represented $cdim class_candidate policy_welfare) (class_appeal2) if work
> ingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  5.129e-30  
Iteration 1:  EE criterion =  6.882e-33  

Causal mediation analysis                                Number of obs = 2,536

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Multivalued
                                               (Std. err. adjusted for 647 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------------
                                       |               Robust
                              selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
NIE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0108558   .0061346    -1.77   0.077    -.0228793    .0011677
(Solidarity appeal vs Turnout appeal)  |  -.0043363   .0058034    -0.75   0.455    -.0157108    .0070383
---------------------------------------+----------------------------------------------------------------
NDE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0603351   .0273437    -2.21   0.027    -.1139278   -.0067424
(Solidarity appeal vs Turnout appeal)  |  -.0436231    .027883    -1.56   0.118    -.0982727    .0110266
---------------------------------------+----------------------------------------------------------------
TE                                     |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0711908   .0279546    -2.55   0.011    -.1259808   -.0164009
(Solidarity appeal vs Turnout appeal)  |  -.0479593     .02865    -1.67   0.094    -.1041123    .0081937
--------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim policy_welfare conflict_appeal solidarity_appeal)(represented $cdim policy_welfare conflict_appeal solida
> rity_appeal) (class_candidate) if workingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  6.452e-30  
Iteration 1:  EE criterion =  8.040e-32  

Causal mediation analysis                                Number of obs = 2,532

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Binary
                                         (Std. err. adjusted for 651 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                        selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
NIE                              |
                 class_candidate |
(Working class vs Middle class)  |   .0000589   .0029573     0.02   0.984    -.0057373     .005855
---------------------------------+----------------------------------------------------------------
NDE                              |
                 class_candidate |
(Working class vs Middle class)  |   .0199327   .0198119     1.01   0.314    -.0188979    .0587634
---------------------------------+----------------------------------------------------------------
TE                               |
                 class_candidate |
(Working class vs Middle class)  |   .0199916   .0200481     1.00   0.319    -.0193019    .0592851
--------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim policy_welfare conflict_appeal solidarity_appeal)(represented $cdim policy_welfare conflict_appeal solida
> rity_appeal) (class_candidate) if workingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  7.008e-30  
Iteration 1:  EE criterion =  4.991e-33  

Causal mediation analysis                                Number of obs = 2,536

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Binary
                                         (Std. err. adjusted for 647 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                        selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
NIE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0105022   .0042159    -2.49   0.013    -.0187652   -.0022393
---------------------------------+----------------------------------------------------------------
NDE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0039772   .0194501    -0.20   0.838    -.0420986    .0341442
---------------------------------+----------------------------------------------------------------
TE                               |
                 class_candidate |
(Working class vs Middle class)  |  -.0144794   .0196989    -0.74   0.462    -.0530886    .0241298
--------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim class_candidate conflict_appeal solidarity_appeal)(represented $cdim class_candidate conflict_appeal soli
> darity_appeal) (policy_welfare) if workingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  5.118e-30  
Iteration 1:  EE criterion =  8.747e-32  

Causal mediation analysis                                Number of obs = 2,532

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Binary
                                                (Std. err. adjusted for 651 clusters in RespondentSerial)
---------------------------------------------------------------------------------------------------------
                                        |               Robust
                               selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------------------+----------------------------------------------------------------
NIE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0116303    .004509     2.58   0.010     .0027928    .0204679
----------------------------------------+----------------------------------------------------------------
NDE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0450263   .0201637     2.23   0.026     .0055062    .0845464
----------------------------------------+----------------------------------------------------------------
TE                                      |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0566566   .0206256     2.75   0.006     .0162312     .097082
---------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim class_candidate conflict_appeal solidarity_appeal)(represented $cdim class_candidate conflict_appeal soli
> darity_appeal) (policy_welfare) if workingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  7.724e-30  
Iteration 1:  EE criterion =  1.851e-31  

Causal mediation analysis                                Number of obs = 2,536

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Binary
                                                (Std. err. adjusted for 647 clusters in RespondentSerial)
---------------------------------------------------------------------------------------------------------
                                        |               Robust
                               selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------------------+----------------------------------------------------------------
NIE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0224556   .0050985     4.40   0.000     .0124628    .0324485
----------------------------------------+----------------------------------------------------------------
NDE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0063697   .0204138     0.31   0.755    -.0336406    .0463799
----------------------------------------+----------------------------------------------------------------
TE                                      |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0288253    .020881     1.38   0.167    -.0121007    .0697513
---------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. 
. ********************************************************************************
. *TABLE A4***********************************************************************
. ********************************************************************************
. *The code produces the estimates for Total effect (TE); Direct effect (NDE); Indirect effect (NIE) as reported in Table A4
. 
. clear 

. use "Data_Britain.dta"

. 
. global cdim age_candidate gender_candidate   rural_candidate   policy_immigration policy_environment policy_eu

. 
. mediate (selected $cdim class_candidate policy_welfare)(represented $cdim class_candidate policy_welfare) (class_appeal2) if work
> ingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  2.187e-29  
Iteration 1:  EE criterion =  5.962e-31  

Causal mediation analysis                                Number of obs = 4,156

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Multivalued
                                             (Std. err. adjusted for 1,039 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------------
                                       |               Robust
                              selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
NIE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0079239    .008227     0.96   0.335    -.0082008    .0240486
(Solidarity appeal vs Turnout appeal)  |   .0043417   .0083074     0.52   0.601    -.0119404    .0206239
---------------------------------------+----------------------------------------------------------------
NDE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |    .033317   .0198745     1.68   0.094    -.0056364    .0722704
(Solidarity appeal vs Turnout appeal)  |  -.0096373   .0201325    -0.48   0.632    -.0490962    .0298217
---------------------------------------+----------------------------------------------------------------
TE                                     |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0412409   .0215538     1.91   0.056    -.0010038    .0834856
(Solidarity appeal vs Turnout appeal)  |  -.0052955   .0218969    -0.24   0.809    -.0482127    .0376216
--------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim class_candidate policy_welfare)(represented $cdim class_candidate policy_welfare) (class_appeal2) if work
> ingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.291e-29  
Iteration 1:  EE criterion =  5.564e-31  

Causal mediation analysis                                Number of obs = 3,848

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Multivalued
                                               (Std. err. adjusted for 962 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------------
                                       |               Robust
                              selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
NIE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0053327   .0071967    -0.74   0.459     -.019438    .0087727
(Solidarity appeal vs Turnout appeal)  |  -.0075395   .0070273    -1.07   0.283    -.0213128    .0062337
---------------------------------------+----------------------------------------------------------------
NDE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0591635    .021209    -2.79   0.005    -.1007325   -.0175946
(Solidarity appeal vs Turnout appeal)  |   -.064743   .0209939    -3.08   0.002    -.1058904   -.0235957
---------------------------------------+----------------------------------------------------------------
TE                                     |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0644962   .0225911    -2.85   0.004    -.1087739   -.0202185
(Solidarity appeal vs Turnout appeal)  |  -.0722826   .0225955    -3.20   0.001    -.1165689   -.0279963
--------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim policy_welfare conflict_appeal solidarity_appeal)(represented $cdim policy_welfare conflict_appeal solida
> rity_appeal) (class_candidate) if workingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  2.556e-29  
Iteration 1:  EE criterion =  4.027e-31  

Causal mediation analysis                                Number of obs = 4,156

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Binary
                                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                        selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
NIE                              |
                 class_candidate |
(Working class vs Middle class)  |   .0239294   .0058907     4.06   0.000     .0123838     .035475
---------------------------------+----------------------------------------------------------------
NDE                              |
                 class_candidate |
(Working class vs Middle class)  |   .0312682    .014449     2.16   0.030     .0029488    .0595877
---------------------------------+----------------------------------------------------------------
TE                               |
                 class_candidate |
(Working class vs Middle class)  |   .0551977   .0155433     3.55   0.000     .0247334     .085662
--------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim policy_welfare conflict_appeal solidarity_appeal)(represented $cdim policy_welfare conflict_appeal solida
> rity_appeal) (class_candidate) if workingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.199e-29  
Iteration 1:  EE criterion =  1.464e-31  

Causal mediation analysis                                Number of obs = 3,848

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Binary
                                         (Std. err. adjusted for 962 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                        selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
NIE                              |
                 class_candidate |
(Working class vs Middle class)  |   -.011772   .0049342    -2.39   0.017    -.0214429   -.0021011
---------------------------------+----------------------------------------------------------------
NDE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0107112   .0153356    -0.70   0.485    -.0407685    .0193461
---------------------------------+----------------------------------------------------------------
TE                               |
                 class_candidate |
(Working class vs Middle class)  |  -.0224832   .0162309    -1.39   0.166    -.0542953    .0093288
--------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim class_candidate conflict_appeal solidarity_appeal)(represented $cdim class_candidate conflict_appeal soli
> darity_appeal) (policy_welfare) if workingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.811e-29  
Iteration 1:  EE criterion =  2.966e-31  

Causal mediation analysis                                Number of obs = 4,156

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Binary
                                              (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------------------------------
                                        |               Robust
                               selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------------------+----------------------------------------------------------------
NIE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0137111   .0063005     2.18   0.030     .0013623    .0260599
----------------------------------------+----------------------------------------------------------------
NDE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0368777   .0146869     2.51   0.012      .008092    .0656635
----------------------------------------+----------------------------------------------------------------
TE                                      |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0505888   .0156903     3.22   0.001     .0198364    .0813413
---------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (selected $cdim class_candidate conflict_appeal solidarity_appeal)(represented $cdim class_candidate conflict_appeal soli
> darity_appeal) (policy_welfare) if workingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.467e-29  
Iteration 1:  EE criterion =  5.504e-32  

Causal mediation analysis                                Number of obs = 3,848

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: represented
Treatment type:    Binary
                                                (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------------------------------
                                        |               Robust
                               selected | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------------------+----------------------------------------------------------------
NIE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |    .017877    .005542     3.23   0.001     .0070148    .0287392
----------------------------------------+----------------------------------------------------------------
NDE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0250572   .0159599     1.57   0.116    -.0062235     .056338
----------------------------------------+----------------------------------------------------------------
TE                                      |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .0429343   .0168502     2.55   0.011     .0099084    .0759601
---------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. 
. 
. ********************************************************************************
. *TABLE A5***********************************************************************
. ********************************************************************************
. *The code produces the estimates for Total effect (TE); Direct effect (NDE); Indirect effect (NIE) as reported in Table A3
. 
. clear

. use "Data_Norway.dta"

. 
. global cdim age_candidate gender_candidate   rural_candidate   policy_immigration policy_environment policy_abort 

. 
. mediate (represented $cdim class_candidate policy_welfare)(rightrating $cdim class_candidate policy_welfare) (class_appeal2) if w
> orkingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.348e-29  
Iteration 1:  EE criterion =  4.479e-32  

Causal mediation analysis                                Number of obs = 2,562

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Multivalued
                                               (Std. err. adjusted for 652 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------------
                                       |               Robust
                           represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
NIE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0001267   .0107797    -0.01   0.991    -.0212547    .0210012
(Solidarity appeal vs Turnout appeal)  |  -5.19e-06   .0004428    -0.01   0.991    -.0008731    .0008627
---------------------------------------+----------------------------------------------------------------
NDE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0290865   .0812445     0.36   0.720    -.1301498    .1883229
(Solidarity appeal vs Turnout appeal)  |   -.103659   .0784068    -1.32   0.186    -.2573335    .0500155
---------------------------------------+----------------------------------------------------------------
TE                                     |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0289598   .0823375     0.35   0.725    -.1324187    .1903383
(Solidarity appeal vs Turnout appeal)  |  -.1036642   .0784326    -1.32   0.186    -.2573894     .050061
--------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim class_candidate policy_welfare)(rightrating $cdim class_candidate policy_welfare) (class_appeal2) if w
> orkingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  2.709e-29  
Iteration 1:  EE criterion =  1.419e-31  

Causal mediation analysis                                Number of obs = 2,566

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Multivalued
                                               (Std. err. adjusted for 651 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------------
                                       |               Robust
                           represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
NIE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0029956   .0112584    -0.27   0.790    -.0250616    .0190703
(Solidarity appeal vs Turnout appeal)  |  -.0005063    .002032    -0.25   0.803    -.0044889    .0034764
---------------------------------------+----------------------------------------------------------------
NDE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.1627139   .0771903    -2.11   0.035    -.3140041   -.0114237
(Solidarity appeal vs Turnout appeal)  |  -.0646167   .0741882    -0.87   0.384    -.2100229    .0807895
---------------------------------------+----------------------------------------------------------------
TE                                     |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.1657095   .0771924    -2.15   0.032    -.3170038   -.0144153
(Solidarity appeal vs Turnout appeal)  |   -.065123   .0742464    -0.88   0.380    -.2106432    .0803973
--------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim policy_welfare conflict_appeal solidarity_appeal)(rightrating $cdim policy_welfare conflict_appeal sol
> idarity_appeal) (class_candidate) if workingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.236e-29  
Iteration 1:  EE criterion =  3.335e-32  

Causal mediation analysis                                Number of obs = 2,562

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Binary
                                         (Std. err. adjusted for 652 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                     represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
NIE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0000545   .0046374    -0.01   0.991    -.0091437    .0090347
---------------------------------+----------------------------------------------------------------
NDE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0038487       .058    -0.07   0.947    -.1175265    .1098291
---------------------------------+----------------------------------------------------------------
TE                               |
                 class_candidate |
(Working class vs Middle class)  |  -.0039032   .0577944    -0.07   0.946    -.1171782    .1093718
--------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim policy_welfare conflict_appeal solidarity_appeal)(rightrating $cdim policy_welfare conflict_appeal sol
> idarity_appeal) (class_candidate) if workingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  4.539e-29  
Iteration 1:  EE criterion =  1.242e-31  

Causal mediation analysis                                Number of obs = 2,566

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Binary
                                         (Std. err. adjusted for 651 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                     represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
NIE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0008995   .0034186    -0.26   0.792    -.0075999    .0058009
---------------------------------+----------------------------------------------------------------
NDE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.1288514   .0528362    -2.44   0.015    -.2324084   -.0252944
---------------------------------+----------------------------------------------------------------
TE                               |
                 class_candidate |
(Working class vs Middle class)  |  -.1297509   .0528533    -2.45   0.014    -.2333414   -.0261604
--------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim class_candidate conflict_appeal solidarity_appeal)(rightrating $cdim class_candidate conflict_appeal s
> olidarity_appeal) (policy_welfare) if workingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.861e-29  
Iteration 1:  EE criterion =  5.735e-32  

Causal mediation analysis                                Number of obs = 2,562

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Binary
                                                (Std. err. adjusted for 652 clusters in RespondentSerial)
---------------------------------------------------------------------------------------------------------
                                        |               Robust
                            represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------------------+----------------------------------------------------------------
NIE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |  -.0002164    .018409    -0.01   0.991    -.0362974    .0358646
----------------------------------------+----------------------------------------------------------------
NDE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .1668093   .0639216     2.61   0.009     .0415253    .2920934
----------------------------------------+----------------------------------------------------------------
TE                                      |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .1665929   .0649722     2.56   0.010     .0392498     .293936
---------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim class_candidate conflict_appeal solidarity_appeal)(rightrating $cdim class_candidate conflict_appeal s
> olidarity_appeal) (policy_welfare) if workingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  4.750e-29  
Iteration 1:  EE criterion =  2.452e-31  

Causal mediation analysis                                Number of obs = 2,566

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Binary
                                                (Std. err. adjusted for 651 clusters in RespondentSerial)
---------------------------------------------------------------------------------------------------------
                                        |               Robust
                            represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------------------+----------------------------------------------------------------
NIE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |  -.0079478   .0296315    -0.27   0.789    -.0660244    .0501289
----------------------------------------+----------------------------------------------------------------
NDE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .2846576   .0618803     4.60   0.000     .1633745    .4059408
----------------------------------------+----------------------------------------------------------------
TE                                      |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .2767099   .0618659     4.47   0.000     .1554548    .3979649
---------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. 
. 
. ********************************************************************************
. *TABLE A6***********************************************************************
. ********************************************************************************
. *The code produces the estimates for Total effect (TE); Direct effect (NDE); Indirect effect (NIE) as reported in Table A4
. 
. clear 

. use "Data_Britain.dta"

. 
. global cdim age_candidate gender_candidate   rural_candidate   policy_immigration policy_environment policy_eu

. 
. mediate (represented $cdim class_candidate policy_welfare)(rightrating $cdim class_candidate policy_welfare) (class_appeal2) if w
> orkingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  9.367e-29  
Iteration 1:  EE criterion =  1.539e-31  

Causal mediation analysis                                Number of obs = 4,156

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Multivalued
                                             (Std. err. adjusted for 1,039 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------------
                                       |               Robust
                           represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
NIE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   -.012319   .0086485    -1.42   0.154    -.0292698    .0046318
(Solidarity appeal vs Turnout appeal)  |   -.007089   .0053442    -1.33   0.185    -.0175634    .0033854
---------------------------------------+----------------------------------------------------------------
NDE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0784412   .0681318     1.15   0.250    -.0550947    .2119772
(Solidarity appeal vs Turnout appeal)  |   .0433192   .0703384     0.62   0.538    -.0945415      .18118
---------------------------------------+----------------------------------------------------------------
TE                                     |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   .0661222   .0683229     0.97   0.333    -.0677882    .2000326
(Solidarity appeal vs Turnout appeal)  |   .0362303   .0704362     0.51   0.607    -.1018221    .1742826
--------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim class_candidate policy_welfare)(rightrating $cdim class_candidate policy_welfare) (class_appeal2) if w
> orkingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.230e-28  
Iteration 1:  EE criterion =  2.933e-31  

Causal mediation analysis                                Number of obs = 3,848

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Multivalued
                                               (Std. err. adjusted for 962 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------------
                                       |               Robust
                           represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
NIE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |   -.046919   .0135006    -3.48   0.001    -.0733798   -.0204583
(Solidarity appeal vs Turnout appeal)  |  -.0161074   .0092782    -1.74   0.083    -.0342924    .0020776
---------------------------------------+----------------------------------------------------------------
NDE                                    |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0048524   .0687404    -0.07   0.944     -.139581    .1298762
(Solidarity appeal vs Turnout appeal)  |  -.0570894   .0679089    -0.84   0.401    -.1901883    .0760096
---------------------------------------+----------------------------------------------------------------
TE                                     |
                         class_appeal2 |
  (Conflict appeal vs Turnout appeal)  |  -.0517714   .0695816    -0.74   0.457    -.1881489     .084606
(Solidarity appeal vs Turnout appeal)  |  -.0731968   .0678841    -1.08   0.281    -.2062472    .0598536
--------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim policy_welfare conflict_appeal solidarity_appeal)(rightrating $cdim policy_welfare conflict_appeal sol
> idarity_appeal) (class_candidate) if workingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.146e-28  
Iteration 1:  EE criterion =  1.236e-31  

Causal mediation analysis                                Number of obs = 4,156

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Binary
                                       (Std. err. adjusted for 1,039 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                     represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
NIE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0091525   .0062552    -1.46   0.143    -.0214125    .0031074
---------------------------------+----------------------------------------------------------------
NDE                              |
                 class_candidate |
(Working class vs Middle class)  |   .2088347   .0484453     4.31   0.000     .1138836    .3037858
---------------------------------+----------------------------------------------------------------
TE                               |
                 class_candidate |
(Working class vs Middle class)  |   .1996822   .0484371     4.12   0.000     .1047472    .2946171
--------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim policy_welfare conflict_appeal solidarity_appeal)(rightrating $cdim policy_welfare conflict_appeal sol
> idarity_appeal) (class_candidate) if workingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.206e-28  
Iteration 1:  EE criterion =  4.016e-32  

Causal mediation analysis                                Number of obs = 3,848

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Binary
                                         (Std. err. adjusted for 962 clusters in RespondentSerial)
--------------------------------------------------------------------------------------------------
                                 |               Robust
                     represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------------------+----------------------------------------------------------------
NIE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0287321   .0090447    -3.18   0.001    -.0464594   -.0110049
---------------------------------+----------------------------------------------------------------
NDE                              |
                 class_candidate |
(Working class vs Middle class)  |  -.0855553   .0474708    -1.80   0.072    -.1785963    .0074857
---------------------------------+----------------------------------------------------------------
TE                               |
                 class_candidate |
(Working class vs Middle class)  |  -.1142874   .0480081    -2.38   0.017    -.2083816   -.0201932
--------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim class_candidate conflict_appeal solidarity_appeal)(rightrating $cdim class_candidate conflict_appeal s
> olidarity_appeal) (policy_welfare) if workingclass==1, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  7.649e-29  
Iteration 1:  EE criterion =  3.265e-31  

Causal mediation analysis                                Number of obs = 4,156

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Binary
                                              (Std. err. adjusted for 1,039 clusters in RespondentSerial)
---------------------------------------------------------------------------------------------------------
                                        |               Robust
                            represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------------------+----------------------------------------------------------------
NIE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |  -.0136667    .009493    -1.44   0.150    -.0322727    .0049394
----------------------------------------+----------------------------------------------------------------
NDE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .1280807    .051884     2.47   0.014     .0263899    .2297715
----------------------------------------+----------------------------------------------------------------
TE                                      |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |    .114414   .0522036     2.19   0.028     .0120969    .2167312
---------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. mediate (represented $cdim class_candidate conflict_appeal solidarity_appeal)(rightrating $cdim class_candidate conflict_appeal s
> olidarity_appeal) (policy_welfare) if workingclass==0, vce(cl RespondentSerial) nointer

Iteration 0:  EE criterion =  1.330e-28  
Iteration 1:  EE criterion =  8.293e-31  

Causal mediation analysis                                Number of obs = 3,848

Outcome model:     Linear
Mediator model:    Linear
Mediator variable: rightrating
Treatment type:    Binary
                                                (Std. err. adjusted for 962 clusters in RespondentSerial)
---------------------------------------------------------------------------------------------------------
                                        |               Robust
                            represented | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------------------+----------------------------------------------------------------
NIE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |  -.0670169   .0149006    -4.50   0.000    -.0962214   -.0378123
----------------------------------------+----------------------------------------------------------------
NDE                                     |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .2405738   .0542475     4.43   0.000     .1342507    .3468969
----------------------------------------+----------------------------------------------------------------
TE                                      |
                         policy_welfare |
(Expand welfare state vs Reduce taxes)  |   .1735569    .053468     3.25   0.001     .0687617    .2783522
---------------------------------------------------------------------------------------------------------
Note: Outcome equation does not include treatment–mediator interaction.

. 
. ********************************************************************************
. *FIGURE A11*********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg selected $dimensions_no if workingclass==0 & hvoter==1, cl(RespondentSerial)

Linear regression                               Number of obs     =        668
                                                F(10, 170)        =       3.88
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0523
                                                Root MSE          =     .49081

                           (Std. err. adjusted for 171 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   -.031507   .0392917    -0.80   0.424    -.1090695    .0460555
  gender_candidate |  -.1297009   .0411314    -3.15   0.002    -.2108949   -.0485068
   class_candidate |    .011504   .0412298     0.28   0.781    -.0698843    .0928923
   rural_candidate |    .004885   .0353317     0.14   0.890    -.0648603    .0746304
    policy_welfare |  -.0869642   .0399033    -2.18   0.031     -.165734   -.0081945
policy_immigration |   .0581607   .0395661     1.47   0.143    -.0199435     .136265
policy_environment |   .0330231    .038484     0.86   0.392     -.042945    .1089913
      policy_abort |  -.0573125   .0403824    -1.42   0.158    -.1370281    .0224031
   conflict_appeal |  -.1770409   .0549045    -3.22   0.002    -.2854232   -.0686586
 solidarity_appeal |  -.1511678   .0547626    -2.76   0.006      -.25927   -.0430656
             _cons |   .7257523   .0614352    11.81   0.000     .6044782    .8470264
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_no if workingclass==1 & hvoter==1, cl(RespondentSerial)

Linear regression                               Number of obs     =        386
                                                F(10, 96)         =       3.60
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0560
                                                Root MSE          =     .49288

                            (Std. err. adjusted for 97 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |    .009098   .0460194     0.20   0.844    -.0822498    .1004458
  gender_candidate |   .0053489   .0505899     0.11   0.916    -.0950712    .1057691
   class_candidate |   .0746074   .0540232     1.38   0.170    -.0326277    .1818426
   rural_candidate |  -.0382876   .0554747    -0.69   0.492     -.148404    .0718288
    policy_welfare |   .1082344   .0565453     1.91   0.059    -.0040072    .2204759
policy_immigration |   .0397454    .049234     0.81   0.422    -.0579833     .137474
policy_environment |   .1361647   .0495649     2.75   0.007     .0377791    .2345503
      policy_abort |  -.1113567   .0520902    -2.14   0.035    -.2147548   -.0079585
   conflict_appeal |   -.033459   .0715239    -0.47   0.641    -.1754329    .1085149
 solidarity_appeal |  -.0739882   .0693917    -1.07   0.289    -.2117297    .0637533
             _cons |   .4283579   .1009511     4.24   0.000     .2279716    .6287441
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid) xsc(r(-.4 .4)) xlab(
> -.4 (.2) .4)  xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) tit
> le("AMCE by class among Høyre voters", size(medsmall))

. graph save "amce_class_h_no.gph", replace
file amce_class_h_no.gph saved

.  
. reg selected $dimensions_interactions_no workingclass if hvoter==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      1,054
                                                F(21, 267)        =       3.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0536
                                                Root MSE          =     .49156

                              (Std. err. adjusted for 268 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |   -.031507    .039349    -0.80   0.424    -.1089807    .0459668
     gender_candidate |  -.1297009   .0411913    -3.15   0.002     -.210802   -.0485998
      class_candidate |    .011504   .0412899     0.28   0.781    -.0697912    .0927992
      rural_candidate |    .004885   .0353832     0.14   0.890    -.0647805    .0745505
       policy_welfare |  -.0869642   .0399614    -2.18   0.030    -.1656438   -.0082847
   policy_immigration |   .0581607   .0396238     1.47   0.143    -.0198541    .1361756
   policy_environment |   .0330231   .0385401     0.86   0.392     -.042858    .1089043
         policy_abort |  -.0573125   .0404413    -1.42   0.158    -.1369369    .0223118
      conflict_appeal |  -.1770409   .0549845    -3.22   0.001    -.2852991   -.0687826
    solidarity_appeal |  -.1511678   .0548423    -2.76   0.006    -.2591463   -.0431893
     age_candidateXwc |   .0406049   .0603258     0.67   0.501    -.0781698    .1593796
  gender_candidateXwc |   .1350498   .0649886     2.08   0.039     .0070945    .2630051
   class_candidateXwc |   .0631035   .0677218     0.93   0.352    -.0702333    .1964402
   rural_candidateXwc |  -.0431726   .0655002    -0.66   0.510    -.1721352      .08579
    policy_welfareXwc |   .1951986   .0689466     2.83   0.005     .0594505    .3309467
policy_immigrationXwc |  -.0184154    .062954    -0.29   0.770    -.1423648     .105534
policy_environmentXwc |   .1031416   .0625362     1.65   0.100    -.0199853    .2262685
      policy_abortXwc |  -.0540441   .0656839    -0.82   0.411    -.1833685    .0752802
   conflict_appealXwc |   .1435818    .089855     1.60   0.111    -.0333326    .3204963
 solidarity_appealXwc |   .0771796   .0881003     0.88   0.382    -.0962801    .2506394
         workingclass |  -.2973945   .1176725    -2.53   0.012    -.5290785   -.0657104
                _cons |   .7257523   .0615247    11.80   0.000      .604617    .8468876
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_no workingclass) ylabel(, nogrid)  xsc(r(-.2 .4)) xlab(-.2 (.2) .4) xline(0, lpat(short
> dash_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by class among Høyre vot
> ers", size(medsmall)) 

. graph save "amce_diff_h_no.gph", replace
file amce_diff_h_no.gph saved

. 
. 
. clear 

. use "Data_Britain.dta"

. 
. reg selected $dimensions_uk if workingclass==0 & vote_consvlab==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      1,336
                                                F(10, 333)        =      10.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0673
                                                Root MSE          =     .48488

                           (Std. err. adjusted for 334 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0127137   .0270333    -0.47   0.638    -.0658913    .0404639
  gender_candidate |  -.0149043   .0280875    -0.53   0.596    -.0701556     .040347
   class_candidate |  -.0540731   .0277878    -1.95   0.053    -.1087349    .0005886
   rural_candidate |   .0069448   .0273041     0.25   0.799    -.0467655     .060655
    policy_welfare |  -.0522288   .0267377    -1.95   0.052    -.1048249    .0003673
policy_immigration |   .1929908   .0263424     7.33   0.000     .1411723    .2448094
policy_environment |  -.0449055   .0275576    -1.63   0.104    -.0991144    .0093033
         policy_eu |   .0831999   .0272514     3.05   0.002     .0295932    .1368066
   conflict_appeal |  -.1803925   .0372353    -4.84   0.000    -.2536386   -.1071465
 solidarity_appeal |  -.1154818   .0368754    -3.13   0.002      -.18802   -.0429437
             _cons |   .5621007   .0503224    11.17   0.000     .4631109    .6610905
------------------------------------------------------------------------------------

. est store wc0

. reg selected $dimensions_uk if workingclass==1 & vote_consvlab==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      1,420
                                                F(10, 354)        =      11.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0814
                                                Root MSE          =     .48108

                           (Std. err. adjusted for 355 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0181421   .0251412     0.72   0.471    -.0313027    .0675869
  gender_candidate |  -.0678135   .0262558    -2.58   0.010    -.1194505   -.0161765
   class_candidate |   .0447992   .0262237     1.71   0.088    -.0067746    .0963731
   rural_candidate |   -.009624   .0268342    -0.36   0.720    -.0623986    .0431505
    policy_welfare |  -.0259883   .0246724    -1.05   0.293    -.0745112    .0225345
policy_immigration |   .2556788   .0276641     9.24   0.000     .2012721    .3100855
policy_environment |  -.0262821   .0260409    -1.01   0.314    -.0774963    .0249322
         policy_eu |   .0886687    .027527     3.22   0.001     .0345317    .1428057
   conflict_appeal |  -.0084249    .033529    -0.25   0.802     -.074366    .0575163
 solidarity_appeal |  -.0216225   .0338738    -0.64   0.524    -.0882417    .0449967
             _cons |   .3756172   .0444981     8.44   0.000     .2881033     .463131
------------------------------------------------------------------------------------

. est store wc1

. 
. coefplot  (wc0, label(Middle class) color(black)) (wc1, label(Working class)),  drop(_cons) ylabel(, nogrid) xsc(r(-.4 .4)) xlab(
> -.4 (.2) .4) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) titl
> e("AMCE by class among Conservative voters", size(medsmall))

. graph save "amce_class_c_uk.gph", replace
file amce_class_c_uk.gph saved

.  
. reg selected $dimensions_interactions_uk workingclass if vote_consvlab==1, cl(RespondentSerial)

Linear regression                               Number of obs     =      2,756
                                                F(21, 688)        =      10.66
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0746
                                                Root MSE          =     .48293

                              (Std. err. adjusted for 689 clusters in RespondentSerial)
---------------------------------------------------------------------------------------
                      |               Robust
             selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        age_candidate |  -.0127137   .0270142    -0.47   0.638    -.0657539    .0403265
     gender_candidate |  -.0149043   .0280677    -0.53   0.596    -.0700128    .0402043
      class_candidate |  -.0540731   .0277682    -1.95   0.052    -.1085936    .0004474
      rural_candidate |   .0069448   .0272848     0.25   0.799    -.0466267    .0605162
       policy_welfare |  -.0522288   .0267188    -1.95   0.051    -.1046891    .0002314
   policy_immigration |   .1929908   .0263238     7.33   0.000     .1413062    .2446755
   policy_environment |  -.0449055   .0275381    -1.63   0.103    -.0989743    .0091633
            policy_eu |   .0831999   .0272322     3.06   0.002     .0297317    .1366681
      conflict_appeal |  -.1803925    .037209    -4.85   0.000    -.2534493   -.1073358
    solidarity_appeal |  -.1154818   .0368494    -3.13   0.002    -.1878325   -.0431311
     age_candidateXwc |   .0308558   .0368965     0.84   0.403    -.0415873     .103299
  gender_candidateXwc |  -.0529092   .0384268    -1.38   0.169    -.1283571    .0225386
   class_candidateXwc |   .0988724   .0381865     2.59   0.010     .0238962    .1738485
   rural_candidateXwc |  -.0165688   .0382618    -0.43   0.665    -.0916928    .0585552
    policy_welfareXwc |   .0262405   .0363612     0.72   0.471    -.0451518    .0976328
policy_immigrationXwc |    .062688   .0381791     1.64   0.101    -.0122735    .1376495
policy_environmentXwc |   .0186234   .0378938     0.49   0.623    -.0557779    .0930248
         policy_euXwc |   .0054688   .0387134     0.14   0.888    -.0705419    .0814794
   conflict_appealXwc |   .1719677   .0500781     3.43   0.001     .0736434     .270292
 solidarity_appealXwc |   .0938593    .050044     1.88   0.061     -.004398    .1921167
         workingclass |  -.1864836   .0671363    -2.78   0.006    -.3183002   -.0546669
                _cons |   .5621007   .0502868    11.18   0.000     .4633667    .6608348
---------------------------------------------------------------------------------------

. est store wc_int

.  
. coefplot  wc_int,  drop(_cons $dimensions_uk workingclass) ylabel(, nogrid) xsc(r(-.2 .4)) xlab(-.2 (.2) .4) xline(0, lpat(shortd
> ash_dot)) legend(off) plotregion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("Differences in AMCE by class among Conservati
> ve voters", size(medsmall)) 

. graph save "amce_diff_c_uk.gph", replace
file amce_diff_c_uk.gph saved

. 
. gr combine "amce_class_h_no" "amce_diff_h_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "cons_no.gph", replace
file cons_no.gph saved

. gr combine "amce_class_c_uk" "amce_diff_c_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "cons_uk.gph", replace
file cons_uk.gph saved

. grc1leg2 "cons_no.gph" "cons_uk.gph",  loff imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA11.gph", replace
file figureA11.gph saved

. gr export  "figureA11.pdf",as(pdf) replace
file figureA11.pdf saved as PDF format

. 
. 
. ********************************************************************************
. *FIGURE A12*********************************************************************
. ********************************************************************************
. 
. clear

. use "Data_Norway.dta"

. 
. reg selected $dimensions_no if workingclass==1 & class_candidate==0, cl(RespondentSerial)
note: class_candidate omitted because of collinearity.

Linear regression                               Number of obs     =      1,231
                                                F(9, 607)         =       1.79
                                                Prob > F          =     0.0671
                                                R-squared         =     0.0129
                                                Root MSE          =     .49863

                           (Std. err. adjusted for 608 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0596684   .0289919    -2.06   0.040    -.1166051   -.0027318
  gender_candidate |   .0083474   .0281172     0.30   0.767    -.0468713    .0635661
   class_candidate |          0  (omitted)
   rural_candidate |   .0113972   .0290309     0.39   0.695    -.0456161    .0684104
    policy_welfare |   .0608036   .0292171     2.08   0.038     .0034246    .1181825
policy_immigration |   .0434111   .0289317     1.50   0.134    -.0134073    .1002295
policy_environment |   .0538293   .0289135     1.86   0.063    -.0029533    .1106119
      policy_abort |  -.0077085   .0280999    -0.27   0.784    -.0628934    .0474764
   conflict_appeal |   .0131807   .0391194     0.34   0.736    -.0636451    .0900065
 solidarity_appeal |  -.0005575   .0383968    -0.01   0.988    -.0759643    .0748492
             _cons |   .4261703   .0488166     8.73   0.000     .3303002    .5220403
------------------------------------------------------------------------------------

. est store cc0

. reg selected $dimensions_no if workingclass==1 & class_candidate==1, cl(RespondentSerial)
note: class_candidate omitted because of collinearity.

Linear regression                               Number of obs     =      1,333
                                                F(9, 617)         =       2.01
                                                Prob > F          =     0.0363
                                                R-squared         =     0.0131
                                                Root MSE          =     .49845

                           (Std. err. adjusted for 618 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0117937   .0275322    -0.43   0.669    -.0658618    .0422744
  gender_candidate |  -.0036705   .0272189    -0.13   0.893    -.0571234    .0497825
   class_candidate |          0  (omitted)
   rural_candidate |    .006136    .027847     0.22   0.826    -.0485503    .0608223
    policy_welfare |    .055201   .0271076     2.04   0.042     .0019666    .1084354
policy_immigration |    .051627   .0274146     1.88   0.060    -.0022103    .1054643
policy_environment |   .0501656   .0280236     1.79   0.074    -.0048677    .1051989
      policy_abort |  -.0066704   .0281065    -0.24   0.812    -.0618663    .0485255
   conflict_appeal |   .0736275   .0375734     1.96   0.050    -.0001597    .1474147
 solidarity_appeal |   .0098857   .0367922     0.27   0.788    -.0623674    .0821387
             _cons |   .4054789   .0473418     8.56   0.000     .3125083    .4984496
------------------------------------------------------------------------------------

. est store cc1

. 
. coefplot  (cc0, label(Middle class candidate) color(black)) (cc1, label(Working class candidate)),  keep(conflict_appeal solidari
> ty_appeal) ylabel(, nogrid) xsc(r(-.15 .15)) xlab(-.15 (.05) .15) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotre
> gion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("AMCE by candidate class among working class voters", size(medsmall))

. graph save "amce_class_cand_wc_no.gph", replace
file amce_class_cand_wc_no.gph saved

. 
. reg selected $dimensions_no if workingclass==0 & class_candidate==0, cl(RespondentSerial)
note: class_candidate omitted because of collinearity.

Linear regression                               Number of obs     =      1,260
                                                F(9, 600)         =       1.24
                                                Prob > F          =     0.2680
                                                R-squared         =     0.0083
                                                Root MSE          =     .49985

                           (Std. err. adjusted for 601 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0308922   .0285309    -1.08   0.279    -.0869248    .0251403
  gender_candidate |  -.0439547   .0281588    -1.56   0.119    -.0992565    .0113471
   class_candidate |          0  (omitted)
   rural_candidate |     .01932   .0298844     0.65   0.518    -.0393707    .0780108
    policy_welfare |   .0188496   .0281655     0.67   0.504    -.0364653    .0741646
policy_immigration |   .0196864   .0283553     0.69   0.488    -.0360013    .0753741
policy_environment |  -.0290701   .0278978    -1.04   0.298    -.0838593     .025719
      policy_abort |  -.0053881   .0282144    -0.19   0.849    -.0607991    .0500229
   conflict_appeal |  -.0792817    .039076    -2.03   0.043    -.1560241   -.0025392
 solidarity_appeal |  -.0668876   .0405666    -1.65   0.100    -.1465574    .0127822
             _cons |   .5915525   .0484102    12.22   0.000     .4964784    .6866266
------------------------------------------------------------------------------------

. est store cc0

. reg selected $dimensions_no if workingclass==0 & class_candidate==1, cl(RespondentSerial)
note: class_candidate omitted because of collinearity.

Linear regression                               Number of obs     =      1,296
                                                F(9, 606)         =       1.66
                                                Prob > F          =     0.0941
                                                R-squared         =     0.0109
                                                Root MSE          =     .49916

                           (Std. err. adjusted for 607 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |    .009164   .0286493     0.32   0.749    -.0471001     .065428
  gender_candidate |  -.0613858    .027735    -2.21   0.027    -.1158542   -.0069175
   class_candidate |          0  (omitted)
   rural_candidate |   .0224426   .0273189     0.82   0.412    -.0312087    .0760939
    policy_welfare |   .0400676   .0281417     1.42   0.155    -.0151996    .0953347
policy_immigration |   .0449566    .027141     1.66   0.098    -.0083453    .0982586
policy_environment |  -.0323527   .0296364    -1.09   0.275    -.0905552    .0258497
      policy_abort |   -.012958   .0282838    -0.46   0.647    -.0685042    .0425882
   conflict_appeal |  -.0582735   .0368947    -1.58   0.115    -.1307305    .0141835
 solidarity_appeal |  -.0233261   .0378154    -0.62   0.538    -.0975913    .0509391
             _cons |    .519739   .0463698    11.21   0.000      .428674     .610804
------------------------------------------------------------------------------------

. est store cc1

. 
. coefplot  (cc0, label(Middle class candidate) color(black)) (cc1, label(Working class candidate)),  keep(conflict_appeal solidari
> ty_appeal) ylabel(, nogrid) xsc(r(-.15 .15)) xlab(-.15 (.05) .15) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotre
> gion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("AMCE by candidate class among middle class voters", size(medsmall))

. graph save "amce_class_cand_mc_no.gph", replace
file amce_class_cand_mc_no.gph saved

. 
. clear 

. use "Data_Britain.dta"

. 
. reg selected $dimensions_uk if workingclass==1 & class_candidate==0, cl(RespondentSerial)
note: class_candidate omitted because of collinearity.

Linear regression                               Number of obs     =      2,136
                                                F(9, 980)         =       7.25
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0290
                                                Root MSE          =     .49312

                           (Std. err. adjusted for 981 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0382359   .0208847    -1.83   0.067    -.0792199     .002748
  gender_candidate |  -.0729767   .0213924    -3.41   0.001    -.1149568   -.0309965
   class_candidate |          0  (omitted)
   rural_candidate |  -.0134332   .0215102    -0.62   0.532    -.0556446    .0287782
    policy_welfare |   .0744605   .0216886     3.43   0.001     .0318991    .1170219
policy_immigration |   .1056735   .0214721     4.92   0.000     .0635369    .1478101
policy_environment |  -.0579219   .0221868    -2.61   0.009    -.1014611   -.0143828
         policy_eu |  -.0189348   .0216644    -0.87   0.382    -.0614488    .0235791
   conflict_appeal |   .0327222   .0298668     1.10   0.274     -.025888    .0913324
 solidarity_appeal |    .017489   .0296834     0.59   0.556    -.0407613    .0757392
             _cons |   .4648889   .0367412    12.65   0.000     .3927884    .5369894
------------------------------------------------------------------------------------

. est store cc0

. reg selected $dimensions_uk if workingclass==1 & class_candidate==1, cl(RespondentSerial)
note: class_candidate omitted because of collinearity.

Linear regression                               Number of obs     =      2,020
                                                F(9, 973)         =       5.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0245
                                                Root MSE          =     .49425

                           (Std. err. adjusted for 974 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0063295   .0223319     0.28   0.777    -.0374948    .0501538
  gender_candidate |   -.022883   .0226153    -1.01   0.312    -.0672633    .0214973
   class_candidate |          0  (omitted)
   rural_candidate |   .0029255   .0223549     0.13   0.896    -.0409439    .0467949
    policy_welfare |   .0216237   .0226183     0.96   0.339    -.0227627      .06601
policy_immigration |   .1147203   .0222441     5.16   0.000     .0710683    .1583723
policy_environment |  -.0670645   .0222366    -3.02   0.003    -.1107016   -.0234274
         policy_eu |  -.0189696   .0226228    -0.84   0.402    -.0633646    .0254254
   conflict_appeal |   .0508522   .0312568     1.63   0.104    -.0104864    .1121907
 solidarity_appeal |   -.028191   .0317673    -0.89   0.375    -.0905313    .0341494
             _cons |    .502553   .0379306    13.25   0.000     .4281179    .5769882
------------------------------------------------------------------------------------

. est store cc1

. 
. coefplot  (cc0, label(Middle class candidate) color(black)) (cc1, label(Working class candidate)),  keep(conflict_appeal solidari
> ty_appeal) ylabel(, nogrid) xsc(r(-.15 .15)) xlab(-.15 (.05) .15) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotre
> gion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("AMCE by candidate class among working class voters", size(medsmall))

. graph save "amce_class_cand_wc_uk.gph", replace
file amce_class_cand_wc_uk.gph saved

. 
.  
. reg selected $dimensions_uk if workingclass==0 & class_candidate==0, cl(RespondentSerial)
note: class_candidate omitted because of collinearity.

Linear regression                               Number of obs     =      1,926
                                                F(9, 905)         =       3.61
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0172
                                                Root MSE          =     .49685

                           (Std. err. adjusted for 906 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |   .0022147   .0230969     0.10   0.924     -.043115    .0475443
  gender_candidate |  -.0176281   .0230058    -0.77   0.444     -.062779    .0275227
   class_candidate |          0  (omitted)
   rural_candidate |    .028733   .0236627     1.21   0.225    -.0177072    .0751732
    policy_welfare |   .0494431   .0223292     2.21   0.027       .00562    .0932662
policy_immigration |   .0368504   .0231916     1.59   0.112    -.0086652    .0823661
policy_environment |  -.0982765   .0237901    -4.13   0.000    -.1449666   -.0515863
         policy_eu |  -.0401186   .0234495    -1.71   0.087    -.0861402    .0059031
   conflict_appeal |  -.0475815   .0323474    -1.47   0.142    -.1110661    .0159031
 solidarity_appeal |  -.0426771   .0316058    -1.35   0.177    -.1047062    .0193521
             _cons |   .5652235   .0411326    13.74   0.000     .4844972    .6459498
------------------------------------------------------------------------------------

. est store cc0

. reg selected $dimensions_uk if workingclass==0 & class_candidate==1, cl(RespondentSerial)
note: class_candidate omitted because of collinearity.

Linear regression                               Number of obs     =      1,922
                                                F(9, 902)         =       7.32
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0331
                                                Root MSE          =     .49283

                           (Std. err. adjusted for 903 clusters in RespondentSerial)
------------------------------------------------------------------------------------
                   |               Robust
          selected | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
     age_candidate |  -.0222177   .0224191    -0.99   0.322    -.0662172    .0217819
  gender_candidate |   .0019196   .0231262     0.08   0.934    -.0434678     .047307
   class_candidate |          0  (omitted)
   rural_candidate |  -.0304531   .0229728    -1.33   0.185    -.0755393    .0146332
    policy_welfare |   .0400632   .0240441     1.67   0.096    -.0071258    .0872522
policy_immigration |   .0606833   .0227303     2.67   0.008     .0160729    .1052937
policy_environment |  -.1200819   .0226238    -5.31   0.000    -.1644832   -.0756805
         policy_eu |    -.08026   .0226061    -3.55   0.000    -.1246265   -.0358934
   conflict_appeal |  -.0781963   .0311795    -2.51   0.012    -.1393892   -.0170034
 solidarity_appeal |  -.0983775   .0312316    -3.15   0.002    -.1596726   -.0370824
             _cons |   .6329761   .0380651    16.63   0.000     .5582696    .7076826
------------------------------------------------------------------------------------

. est store cc1

. 
. coefplot  (cc0, label(Middle class candidate) color(black)) (cc1, label(Working class candidate)),  keep(conflict_appeal solidari
> ty_appeal) ylabel(, nogrid) xsc(r(-.15 .15)) xlab(-.15 (.05) .15) xline(0, lpat(shortdash_dot)) legend(region(col(white))) plotre
> gion(style(none)) lwidth(vvthin) ms(O) scale(.8) title("AMCE by candidate class among middle class voters", size(medsmall))

. graph save "amce_class_cand_mc_uk.gph", replace
file amce_class_cand_mc_uk.gph saved

. 
. gr combine  "amce_class_cand_wc_no.gph" "amce_class_cand_mc_no.gph",   imargin(small)   title("Norway", size(small))

. gr save "class_int_no.gph", replace
file class_int_no.gph saved

. gr combine "amce_class_cand_wc_uk.gph" "amce_class_cand_mc_uk.gph",   imargin(small)   title("Britain", size(small))

. gr save "class_int_uk.gph", replace
file class_int_uk.gph saved

. grc1leg2 "class_int_no.gph" "class_int_uk.gph",  leg("class_int_no.gph") imargin(tiny)  labsize(1.4)  lms(tiny) col(1)
-grc1leg2- working...

. gr_edit plotregion1.graph1.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. gr_edit plotregion1.graph2.plotregion1.graph2.yaxis1.draw_view.setstyle, style(no)

. 
. gr save "figureA12.gph", replace
file figureA12.gph saved

. gr export  "figureA12.pdf",as(pdf) replace
file figureA12.pdf saved as PDF format

. 
end of do-file

. log close
      name:  <unnamed>
       log:  C:\Users\finseraa\Dropbox\05 Group Appeals\replication archive\JOP Dataverse\jop_log.log
  log type:  text
 closed on:  11 Dec 2024, 13:41:52
-----------------------------------------------------------------------------------------------------------------------------------
